├── .gitignore ├── README.md ├── backend ├── .env.sample ├── AI │ ├── AIService.py │ ├── agents │ │ ├── ben_graham.py │ │ ├── bill_ackman.py │ │ ├── cathie_wood.py │ │ ├── charlie_munger.py │ │ ├── fundamentals.py │ │ ├── michael_burry.py │ │ ├── peter_lynch.py │ │ ├── phil_fisher.py │ │ ├── portfolio_manager.py │ │ ├── risk_manager.py │ │ ├── sentiment.py │ │ ├── stanley_druckenmiller.py │ │ ├── technicals.py │ │ ├── valuation.py │ │ └── warren_buffett.py │ ├── backtester.py │ ├── data │ │ ├── cache.py │ │ └── models.py │ ├── graph │ │ └── state.py │ ├── llm │ │ └── models.py │ ├── tools │ │ └── api.py │ └── utils │ │ ├── __init__.py │ │ ├── analysts.py │ │ ├── display.py │ │ ├── llm.py │ │ ├── ollama.py │ │ ├── progress.py │ │ └── visualize.py ├── __init__.py ├── config.py ├── daily_data │ ├── M1101.DCE_future_daily_20100101_20251231.csv │ ├── M1105.DCE_future_daily_20100101_20251231.csv │ ├── M1109.DCE_future_daily_20100101_20251231.csv │ ├── M1201.DCE_future_daily_20100101_20251231.csv │ ├── M1205.DCE_future_daily_20100101_20251231.csv │ ├── M1209.DCE_future_daily_20100101_20251231.csv │ ├── M1301.DCE_future_daily_20100101_20251231.csv │ ├── M1305.DCE_future_daily_20100101_20251231.csv │ ├── M1309.DCE_future_daily_20100101_20251231.csv │ ├── M1401.DCE_future_daily_20100101_20251231.csv │ ├── M1405.DCE_future_daily_20100101_20251231.csv │ ├── M1409.DCE_future_daily_20100101_20251231.csv │ ├── M1501.DCE_future_daily_20100101_20251231.csv │ ├── M1505.DCE_future_daily_20100101_20251231.csv │ ├── M1509.DCE_future_daily_20100101_20251231.csv │ ├── M1601.DCE_future_daily_20100101_20251231.csv │ ├── M1605.DCE_future_daily_20100101_20251231.csv │ ├── M1609.DCE_future_daily_20100101_20251231.csv │ ├── M1701.DCE_future_daily_20100101_20251231.csv │ ├── M1705.DCE_future_daily_20100101_20251231.csv │ ├── M1709.DCE_future_daily_20100101_20251231.csv │ ├── M1801.DCE_future_daily_20100101_20251231.csv │ ├── M1805.DCE_future_daily_20100101_20251231.csv │ ├── M1809.DCE_future_daily_20100101_20251231.csv │ ├── M1901.DCE_future_daily_20100101_20251231.csv │ ├── M1905.DCE_future_daily_20100101_20251231.csv │ ├── M1909.DCE_future_daily_20100101_20251231.csv │ ├── M2001.DCE_future_daily_20100101_20251231.csv │ ├── M2005.DCE_future_daily_20100101_20251231.csv │ ├── M2009.DCE_future_daily_20100101_20251231.csv │ ├── M2101.DCE_future_daily_20100101_20251231.csv │ ├── M2105.DCE_future_daily_20100101_20251231.csv │ ├── M2109.DCE_future_daily_20100101_20251231.csv │ ├── M2201.DCE_future_daily_20100101_20251231.csv │ ├── M2205.DCE_future_daily_20100101_20251231.csv │ ├── M2209.DCE_future_daily_20100101_20251231.csv │ ├── M2301.DCE_future_daily_20100101_20251231.csv │ ├── M2305.DCE_future_daily_20100101_20251231.csv │ ├── M2309.DCE_future_daily_20100101_20251231.csv │ ├── M2401.DCE_future_daily_20100101_20251231.csv │ ├── M2405.DCE_future_daily_20100101_20251231.csv │ ├── M2409.DCE_future_daily_20100101_20251231.csv │ ├── M2501.DCE_future_daily_20100101_20251231.csv │ ├── M2505.DCE_future_daily_20100101_20251231.csv │ ├── M2509.DCE_future_daily_20100101_20251231.csv │ └── M2601.DCE_future_daily_20100101_20251231.csv ├── data │ ├── 159985.SZ_fund_30min_20190101_20251231.csv │ ├── 159985.SZ_fund_daily_20190101_20251231.csv │ ├── 159985.SZ_fund_weekly_20190101_20251231.csv │ ├── B2501.DCE_future_5min_20240801_20241130.csv │ ├── M2501.DCE_future_15min_20240101_20251231.csv │ ├── M2501.DCE_future_30min_20240101_20251231.csv │ ├── M2501.DCE_future_5min_20240101_20251231.csv │ ├── M2501.DCE_future_5min_20240801_20241130.csv │ ├── M2501.DCE_future_60min_20240101_20251231.csv │ ├── M2501.DCE_future_daily_20240101_20251231.csv │ └── Y2501.DCE_future_5min_20240801_20241130.csv ├── init_db.py ├── main.py ├── models │ ├── __init__.py │ ├── account.py │ ├── core_factor.py │ ├── fundamental.py │ ├── kline.py │ ├── market_data.py │ ├── news.py │ ├── position.py │ ├── signals.py │ ├── soybean.py │ ├── trading.py │ └── trading_strategy.py ├── requirements.txt ├── routers │ ├── __init__.py │ ├── account.py │ ├── ai.py │ ├── arbitrage.py │ ├── core_factor.py │ ├── dual_ma.py │ ├── fundamental.py │ ├── grid.py │ ├── holding_analysis.py │ ├── market_data.py │ ├── news.py │ ├── obv_adx_ema.py │ ├── signals.py │ ├── soybean.py │ ├── stockfutures.py │ ├── support_resistance.py │ ├── trading.py │ ├── trading_model.py │ └── trend_follow.py ├── services │ ├── __init__.py │ ├── account.py │ ├── core_factor.py │ ├── fundamental.py │ ├── market_data.py │ ├── news_service.py │ ├── opt_service.py │ ├── position.py │ ├── signals.py │ ├── soybean.py │ ├── stockfutures.py │ ├── support_resistance.py │ └── trading.py ├── strategies │ ├── dual_ma_strategy.py │ ├── grid_strategy.py │ ├── obv_adx_ema_strategy.py │ ├── support_resistance_strategy.py │ └── trend_follow_strategy.py ├── test │ └── test.py ├── tools │ ├── __init__.py │ ├── data_fetcher.py │ └── feedtrade_crawler.py ├── trading.db └── utils │ ├── __init__.py │ └── logger.py ├── docs └── 多品种套利.md └── frontend ├── package-lock.json ├── package.json ├── postcss.config.js ├── public ├── donate.jpg ├── favicon.ico ├── images │ └── experts │ │ ├── ben-graham.jpg │ │ ├── bill-ackman.jpg │ │ ├── cathie-wood.jpg │ │ ├── charlie-munger.jpg │ │ ├── michael-burry.jpg │ │ ├── peter-lynch.jpg │ │ ├── phil-fisher.jpg │ │ ├── stanley-druckenmiller.jpg │ │ └── warren-buffett.jpg ├── index.html ├── logo.png ├── logo192.png └── manifest.json ├── src ├── App.tsx ├── api │ ├── ai.ts │ ├── arbitrage.ts │ ├── signals.ts │ └── soybean.ts ├── components │ ├── KLineChart.tsx │ ├── SRLevels.tsx │ ├── Signallet.css │ ├── Signallet.tsx │ ├── Toast.css │ ├── Toast.tsx │ ├── analysis │ │ ├── CoreFactorAnalysis.tsx │ │ └── StandardizedAnalysis.tsx │ ├── home │ │ ├── FuturesKLineChart.tsx │ │ ├── InventoryInfo.tsx │ │ └── TimeRangeSelector.tsx │ ├── layout │ │ ├── Footer.tsx │ │ ├── Header.tsx │ │ └── Layout.tsx │ ├── market │ │ ├── HistoricalComparison.tsx │ │ ├── InventoryChart.tsx │ │ ├── MarketOverview.tsx │ │ ├── OptionData.tsx │ │ ├── PriceChart.tsx │ │ ├── TechnicalChart.tsx │ │ └── TechnicalIndicators.tsx │ └── trading │ │ ├── ArbitrageStrategy.tsx │ │ ├── ETFStrategy.tsx │ │ ├── FuturesOptionsHedgeStrategy.tsx │ │ ├── GridStrategy.tsx │ │ ├── InterSpeciesArbitrageStrategy.tsx │ │ ├── OBVADXEMAStrategy.tsx │ │ ├── OptionsStrategy.tsx │ │ ├── StrategyAdvice.tsx │ │ ├── SupportResistanceStrategy.tsx │ │ └── TrendFollowStrategy.tsx ├── config │ └── api.ts ├── index.css ├── index.tsx ├── pages │ ├── Agents.tsx │ ├── CoreFactorAnalysisPage.tsx │ ├── Disclaimer.tsx │ ├── HoldingAnalysis.tsx │ ├── Home.tsx │ ├── MarketView.tsx │ ├── MultiVarietyArbitrage.tsx │ ├── NewsAnalysis.tsx │ ├── OptionsStrategyPage.tsx │ ├── PrivacyPolicy.tsx │ ├── ProAnalysis.tsx │ ├── Research.tsx │ ├── Signals.css │ ├── Signals.tsx │ ├── StrategyAdvicePage.tsx │ ├── TermsOfService.tsx │ ├── Trading.tsx │ └── pro │ │ ├── SoybeanImport.tsx │ │ └── StockFutures.tsx └── types │ ├── market.ts │ └── stockFutures.ts ├── tailwind.config.js └── tsconfig.json /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 98 | __pypackages__/ 99 | 100 | # Celery stuff 101 | celerybeat-schedule 102 | celerybeat.pid 103 | 104 | # SageMath parsed files 105 | *.sage.py 106 | 107 | # Environments 108 | .env 109 | .venv 110 | env/ 111 | venv/ 112 | ENV/ 113 | env.bak/ 114 | venv.bak/ 115 | 116 | # Spyder project settings 117 | .spyderproject 118 | .spyproject 119 | 120 | # Rope project settings 121 | .ropeproject 122 | 123 | # mkdocs documentation 124 | /site 125 | 126 | # mypy 127 | .mypy_cache/ 128 | .dmypy.json 129 | dmypy.json 130 | 131 | # Pyre type checker 132 | .pyre/ 133 | 134 | # pytype static type analyzer 135 | .pytype/ 136 | 137 | # Cython debug symbols 138 | cython_debug/ 139 | 140 | node_modules/ 141 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 豆粕品种量化交易策略平台 2 | 3 | ## 项目简介 4 | 5 | 这是一个专注于豆粕品种交易的量化交易策略平台。平台整合了豆粕ETF、豆粕期货和期权的交易分析，为投资者提供全方位的交易决策支持。 6 | 访问网站立即体验：https://www.singzquant.com/ 7 | 8 | ## 核心功能 9 | 10 | - **核心观点**: 提供豆粕市场的核心交易观点和策略建议 11 | - **每日分析**: 实时更新市场动态，技术面和基本面分析 12 | - **AI量化模型**: 基于深度强化学习和多智能体的交易策略辅助系统 13 | - 功能截图 14 | ![image](https://github.com/user-attachments/assets/e1d5a3d5-cfb6-466a-8cf9-400cb58d5800) 15 | ![image](https://github.com/user-attachments/assets/02504afe-fe54-4b20-b4ad-4a6f38d57b45) 16 | ![image](https://github.com/user-attachments/assets/5879b683-1846-4a2c-86a3-ddf540b43e4f) 17 | ![image](https://github.com/user-attachments/assets/833a6ad6-48d3-4d6b-908e-6550bcf84326) 18 | ![image](https://github.com/user-attachments/assets/469b615c-aef9-4aaf-8ab4-393dc23ab04e) 19 | 20 | ## 技术架构 21 | 22 | ### 前端技术栈 23 | - React + TypeScript 24 | - TailwindCSS 25 | - Ant Design Pro 26 | - ECharts 图表库 27 | - WebSocket实时数据 28 | 29 | ### 后端技术栈 30 | - Python FastAPI 31 | - SQLite数据库 32 | - Tushare数据接口 33 | - PyTorch深度学习框架 34 | - 日志监控系统 35 | 36 | ## 快速开始 37 | 38 | ### 环境要求 39 | - Node.js >= 16 40 | - Python >= 3.8 41 | - pip 42 | - npm 或 yarn 43 | 44 | ### 前端部署 45 | ```bash 46 | cd frontend 47 | npm install 48 | npm start 49 | ``` 50 | 51 | ### 后端部署 52 | ```bash 53 | cd backend 54 | pip install -r requirements.txt 55 | uvicorn main:app 56 | ``` 57 | 58 | ### 环境变量配置 59 | 1. 在backend目录下复制`.env.sample`为`.env` 60 | 2. 配置必要的环境变量： 61 | - TUSHARE_TOKEN 62 | - DATABASE_URL 63 | - API_KEY等 64 | 65 | ## 项目结构 66 | ``` 67 | singz_quant/ 68 | ├── frontend/ # React前端项目 69 | │ ├── src/ # 源代码 70 | │ ├── public/ # 静态资源 71 | │ └── build/ # 构建输出 72 | ├── backend/ # FastAPI后端服务 73 | │ ├── models/ # 数据模型 74 | │ ├── routers/ # API路由 75 | │ ├── services/ # 业务逻辑 76 | │ └── utils/ # 工具函数 77 | └── docs/ # 项目文档 78 | ``` 79 | 80 | ## 其他 81 | 如果你喜欢我的项目，可以给我买杯咖啡： 82 | image 83 | 84 | ## 风险提示 85 | 86 | 本系统仅供学习和研究使用，不构成任何投资建议。使用本系统进行实盘交易需要自行承担风险。 87 | 88 | ## 许可证 89 | 90 | MIT License 91 | -------------------------------------------------------------------------------- /backend/.env.sample: -------------------------------------------------------------------------------- 1 | # Tushare API配置 2 | TUSHARE_TOKEN=your_tushare_token_here 3 | 4 | # Deepseek API配置 5 | DEEPSEEK_API_KEY=your_deepseek_api_key_here 6 | OPENAI_API_KEY=your_openai_api_key_here 7 | OPENAI_BASE_URL=your_openai_base_url_here 8 | 9 | # 应用配置 10 | PROJECT_NAME=新致量化策略 11 | API_V1_STR=/api/v1 12 | 13 | # 数据库配置（如果需要） 14 | # DATABASE_URL=postgresql://user:password@localhost:5432/dbname 15 | 16 | # 日志配置 17 | LOG_LEVEL=INFO 18 | LOG_FILE_PATH=logs/app.log 19 | 20 | # 安全配置 21 | SECRET_KEY=your_secret_key_here 22 | ALGORITHM=HS256 23 | ACCESS_TOKEN_EXPIRE_MINUTES=30 -------------------------------------------------------------------------------- /backend/AI/agents/risk_manager.py: -------------------------------------------------------------------------------- 1 | from langchain_core.messages import HumanMessage 2 | from AI.graph.state import AgentState, show_agent_reasoning 3 | from AI.utils.progress import progress 4 | from AI.tools.api import get_prices, prices_to_df 5 | from loguru import logger 6 | import json 7 | from langchain_core.prompts import ChatPromptTemplate 8 | from pydantic import BaseModel 9 | 10 | 11 | ##### 风险管理代理 ##### 12 | def risk_management_agent(state: AgentState): 13 | """ 14 | 基于多个股票的实时风险因素控制仓位大小 15 | 16 | 主要功能: 17 | 1. 获取股票价格数据 18 | 2. 计算投资组合价值 19 | 3. 确定单个股票的最大仓位限制 20 | 4. 考虑当前持仓和可用现金 21 | 5. 生成风险分析报告 22 | """ 23 | logger.info("开始风险管理代理") 24 | portfolio = state["data"]["portfolio"] 25 | data = state["data"] 26 | tickers = data["tickers"] 27 | logger.info(f"处理股票: {tickers}") 28 | 29 | # 初始化每个股票的风险分析 30 | risk_analysis = {} 31 | current_prices = {} # 存储价格以避免重复API调用 32 | 33 | for ticker in tickers: 34 | progress.update_status("risk_management_agent", ticker, "分析价格数据") 35 | logger.info(f"开始分析 {ticker} 的风险因素") 36 | 37 | prices = get_prices( 38 | ticker=ticker, 39 | start_date=data["start_date"], 40 | end_date=data["end_date"], 41 | ) 42 | logger.debug(f"获取到价格数据: {prices}") 43 | 44 | if not prices: 45 | progress.update_status("risk_management_agent", ticker, "失败：未找到价格数据") 46 | logger.error(f"无法获取 {ticker} 的价格数据") 47 | continue 48 | 49 | prices_df = prices_to_df(prices) 50 | logger.debug(f"价格数据转换为DataFrame: {prices_df}") 51 | 52 | progress.update_status("risk_management_agent", ticker, "计算仓位限制") 53 | logger.info(f"计算 {ticker} 的仓位限制") 54 | 55 | # 计算投资组合价值 56 | current_price = prices_df["close"].iloc[-1] 57 | current_prices[ticker] = current_price # 存储当前价格 58 | logger.debug(f"当前价格: {current_price}") 59 | 60 | # 计算该股票的当前仓位价值 61 | current_position_value = portfolio.get("cost_basis", {}).get(ticker, 0) 62 | logger.debug(f"当前仓位价值: {current_position_value}") 63 | 64 | # 使用存储的价格计算总投资组合价值 65 | total_portfolio_value = portfolio.get("cash", 0) + sum(portfolio.get("cost_basis", {}).get(t, 0) for t in portfolio.get("cost_basis", {})) 66 | logger.debug(f"总投资组合价值: {total_portfolio_value}") 67 | 68 | # 基础限制是任何单个仓位的投资组合的20% 69 | position_limit = total_portfolio_value * 0.20 70 | logger.debug(f"单个仓位限制: {position_limit}") 71 | 72 | # 对于现有仓位，从限制中减去当前仓位价值 73 | remaining_position_limit = position_limit - current_position_value 74 | logger.debug(f"剩余仓位限制: {remaining_position_limit}") 75 | 76 | # 确保不超过可用现金 77 | available_cash = portfolio.get("cash", 0) 78 | max_position_size = min(remaining_position_limit, available_cash) 79 | logger.debug(f"可用现金: {available_cash}, 最大仓位大小: {max_position_size}") 80 | 81 | risk_analysis[ticker] = { 82 | "remaining_position_limit": float(max_position_size), 83 | "current_price": float(current_price), 84 | "reasoning": { 85 | "portfolio_value": float(total_portfolio_value), 86 | "current_position": float(current_position_value), 87 | "position_limit": float(position_limit), 88 | "remaining_limit": float(remaining_position_limit), 89 | "available_cash": float(available_cash), 90 | }, 91 | } 92 | logger.info(f"风险分析结果: {risk_analysis[ticker]}") 93 | 94 | progress.update_status("risk_management_agent", ticker, "完成") 95 | logger.info(f"完成 {ticker} 的风险分析") 96 | 97 | message = HumanMessage( 98 | content=json.dumps(risk_analysis), 99 | name="risk_management_agent", 100 | ) 101 | 102 | if state["metadata"]["show_reasoning"]: 103 | show_agent_reasoning(risk_analysis, "风险管理代理") 104 | 105 | # 将信号添加到analyst_signals列表 106 | state["data"]["analyst_signals"]["risk_management_agent"] = risk_analysis 107 | logger.info("风险管理分析完成，返回结果") 108 | 109 | return { 110 | "messages": state["messages"] + [message], 111 | "data": data, 112 | } 113 | -------------------------------------------------------------------------------- /backend/AI/agents/sentiment.py: -------------------------------------------------------------------------------- 1 | from langchain_core.messages import HumanMessage 2 | from AI.graph.state import AgentState, show_agent_reasoning 3 | from AI.utils.progress import progress 4 | import pandas as pd 5 | import numpy as np 6 | import json 7 | 8 | from AI.tools.api import get_insider_trades, get_company_news 9 | 10 | 11 | ##### Sentiment Agent ##### 12 | def sentiment_agent(state: AgentState): 13 | """Analyzes market sentiment and generates trading signals for multiple tickers.""" 14 | data = state.get("data", {}) 15 | end_date = data.get("end_date") 16 | tickers = data.get("tickers") 17 | 18 | # Initialize sentiment analysis for each ticker 19 | sentiment_analysis = {} 20 | 21 | for ticker in tickers: 22 | progress.update_status("sentiment_agent", ticker, "Fetching insider trades") 23 | 24 | # Get the insider trades 25 | insider_trades = get_insider_trades( 26 | ticker=ticker, 27 | end_date=end_date, 28 | limit=1000, 29 | ) 30 | 31 | progress.update_status("sentiment_agent", ticker, "Analyzing trading patterns") 32 | 33 | # Get the signals from the insider trades 34 | transaction_shares = pd.Series([t.transaction_shares for t in insider_trades]).dropna() 35 | insider_signals = np.where(transaction_shares < 0, "bearish", "bullish").tolist() 36 | 37 | progress.update_status("sentiment_agent", ticker, "Fetching company news") 38 | 39 | # Get the company news 40 | company_news = get_company_news(ticker, end_date, limit=100) 41 | 42 | # Get the sentiment from the company news 43 | sentiment = pd.Series([n.sentiment for n in company_news]).dropna() 44 | news_signals = np.where(sentiment == "negative", "bearish", 45 | np.where(sentiment == "positive", "bullish", "neutral")).tolist() 46 | 47 | progress.update_status("sentiment_agent", ticker, "Combining signals") 48 | # Combine signals from both sources with weights 49 | insider_weight = 0.3 50 | news_weight = 0.7 51 | 52 | # Calculate weighted signal counts 53 | bullish_signals = ( 54 | insider_signals.count("bullish") * insider_weight + 55 | news_signals.count("bullish") * news_weight 56 | ) 57 | bearish_signals = ( 58 | insider_signals.count("bearish") * insider_weight + 59 | news_signals.count("bearish") * news_weight 60 | ) 61 | 62 | if bullish_signals > bearish_signals: 63 | overall_signal = "bullish" 64 | elif bearish_signals > bullish_signals: 65 | overall_signal = "bearish" 66 | else: 67 | overall_signal = "neutral" 68 | 69 | # Calculate confidence level based on the weighted proportion 70 | total_weighted_signals = len(insider_signals) * insider_weight + len(news_signals) * news_weight 71 | confidence = 0 # Default confidence when there are no signals 72 | if total_weighted_signals > 0: 73 | confidence = round(max(bullish_signals, bearish_signals) / total_weighted_signals, 2) * 100 74 | reasoning = f"Weighted Bullish signals: {bullish_signals:.1f}, Weighted Bearish signals: {bearish_signals:.1f}" 75 | 76 | sentiment_analysis[ticker] = { 77 | "signal": overall_signal, 78 | "confidence": confidence, 79 | "reasoning": reasoning, 80 | } 81 | 82 | progress.update_status("sentiment_agent", ticker, "Done") 83 | 84 | # Create the sentiment message 85 | message = HumanMessage( 86 | content=json.dumps(sentiment_analysis), 87 | name="sentiment_agent", 88 | ) 89 | 90 | # Print the reasoning if the flag is set 91 | if state["metadata"]["show_reasoning"]: 92 | show_agent_reasoning(sentiment_analysis, "Sentiment Analysis Agent") 93 | 94 | # Add the signal to the analyst_signals list 95 | state["data"]["analyst_signals"]["sentiment_agent"] = sentiment_analysis 96 | 97 | return { 98 | "messages": [message], 99 | "data": data, 100 | } 101 | -------------------------------------------------------------------------------- /backend/AI/data/cache.py: -------------------------------------------------------------------------------- 1 | class Cache: 2 | """In-memory cache for API responses.""" 3 | 4 | def __init__(self): 5 | self._prices_cache: dict[str, list[dict[str, any]]] = {} 6 | self._financial_metrics_cache: dict[str, list[dict[str, any]]] = {} 7 | self._line_items_cache: dict[str, list[dict[str, any]]] = {} 8 | self._insider_trades_cache: dict[str, list[dict[str, any]]] = {} 9 | self._company_news_cache: dict[str, list[dict[str, any]]] = {} 10 | 11 | def _merge_data(self, existing: list[dict] | None, new_data: list[dict], key_field: str) -> list[dict]: 12 | """Merge existing and new data, avoiding duplicates based on a key field.""" 13 | if not existing: 14 | return new_data 15 | 16 | # Create a set of existing keys for O(1) lookup 17 | existing_keys = {item[key_field] for item in existing} 18 | 19 | # Only add items that don't exist yet 20 | merged = existing.copy() 21 | merged.extend([item for item in new_data if item[key_field] not in existing_keys]) 22 | return merged 23 | 24 | def get_prices(self, ticker: str) -> list[dict[str, any]] | None: 25 | """Get cached price data if available.""" 26 | return self._prices_cache.get(ticker) 27 | 28 | def set_prices(self, ticker: str, data: list[dict[str, any]]): 29 | """Append new price data to cache.""" 30 | self._prices_cache[ticker] = self._merge_data(self._prices_cache.get(ticker), data, key_field="time") 31 | 32 | def get_financial_metrics(self, ticker: str) -> list[dict[str, any]]: 33 | """Get cached financial metrics if available.""" 34 | return self._financial_metrics_cache.get(ticker) 35 | 36 | def set_financial_metrics(self, ticker: str, data: list[dict[str, any]]): 37 | """Append new financial metrics to cache.""" 38 | self._financial_metrics_cache[ticker] = self._merge_data(self._financial_metrics_cache.get(ticker), data, key_field="report_period") 39 | 40 | def get_line_items(self, ticker: str) -> list[dict[str, any]] | None: 41 | """Get cached line items if available.""" 42 | return self._line_items_cache.get(ticker) 43 | 44 | def set_line_items(self, ticker: str, data: list[dict[str, any]]): 45 | """Append new line items to cache.""" 46 | self._line_items_cache[ticker] = self._merge_data(self._line_items_cache.get(ticker), data, key_field="report_period") 47 | 48 | def get_insider_trades(self, ticker: str) -> list[dict[str, any]] | None: 49 | """Get cached insider trades if available.""" 50 | return self._insider_trades_cache.get(ticker) 51 | 52 | def set_insider_trades(self, ticker: str, data: list[dict[str, any]]): 53 | """Append new insider trades to cache.""" 54 | self._insider_trades_cache[ticker] = self._merge_data(self._insider_trades_cache.get(ticker), data, key_field="filing_date") # Could also use transaction_date if preferred 55 | 56 | def get_company_news(self, ticker: str) -> list[dict[str, any]] | None: 57 | """Get cached company news if available.""" 58 | return self._company_news_cache.get(ticker) 59 | 60 | def set_company_news(self, ticker: str, data: list[dict[str, any]]): 61 | """Append new company news to cache.""" 62 | self._company_news_cache[ticker] = self._merge_data(self._company_news_cache.get(ticker), data, key_field="date") 63 | 64 | 65 | # Global cache instance 66 | _cache = Cache() 67 | 68 | 69 | def get_cache() -> Cache: 70 | """Get the global cache instance.""" 71 | return _cache 72 | -------------------------------------------------------------------------------- /backend/AI/data/models.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | 3 | 4 | class Price(BaseModel): 5 | open: float 6 | close: float 7 | high: float 8 | low: float 9 | volume: int 10 | time: str 11 | 12 | 13 | class PriceResponse(BaseModel): 14 | ticker: str 15 | prices: list[Price] 16 | 17 | 18 | class FinancialMetrics(BaseModel): 19 | ticker: str 20 | report_period: str 21 | period: str 22 | currency: str 23 | market_cap: float | None 24 | enterprise_value: float | None 25 | price_to_earnings_ratio: float | None 26 | price_to_book_ratio: float | None 27 | price_to_sales_ratio: float | None 28 | enterprise_value_to_ebitda_ratio: float | None 29 | enterprise_value_to_revenue_ratio: float | None 30 | free_cash_flow_yield: float | None 31 | peg_ratio: float | None 32 | gross_margin: float | None 33 | operating_margin: float | None 34 | net_margin: float | None 35 | return_on_equity: float | None 36 | return_on_assets: float | None 37 | return_on_invested_capital: float | None 38 | asset_turnover: float | None 39 | inventory_turnover: float | None 40 | receivables_turnover: float | None 41 | days_sales_outstanding: float | None 42 | operating_cycle: float | None 43 | working_capital_turnover: float | None 44 | current_ratio: float | None 45 | quick_ratio: float | None 46 | cash_ratio: float | None 47 | operating_cash_flow_ratio: float | None 48 | debt_to_equity: float | None 49 | debt_to_assets: float | None 50 | interest_coverage: float | None 51 | revenue_growth: float | None 52 | earnings_growth: float | None 53 | book_value_growth: float | None 54 | earnings_per_share_growth: float | None 55 | free_cash_flow_growth: float | None 56 | operating_income_growth: float | None 57 | ebitda_growth: float | None 58 | payout_ratio: float | None 59 | earnings_per_share: float | None 60 | book_value_per_share: float | None 61 | free_cash_flow_per_share: float | None 62 | 63 | 64 | class FinancialMetricsResponse(BaseModel): 65 | financial_metrics: list[FinancialMetrics] 66 | 67 | 68 | class LineItem(BaseModel): 69 | ticker: str 70 | report_period: str 71 | period: str 72 | currency: str 73 | revenue: float | None = None 74 | earnings_per_share: float | None = None 75 | net_income: float | None = None 76 | free_cash_flow: float | None = None 77 | operating_margin: float | None = None 78 | depreciation_and_amortization: float | None = None 79 | total_assets: float | None = None 80 | total_liabilities: float | None = None 81 | current_assets: float | None = None 82 | current_liabilities: float | None = None 83 | book_value_per_share: float | None = None 84 | dividends_and_other_cash_distributions: float | None = None 85 | outstanding_shares: float | None = None 86 | # Allow additional fields dynamically 87 | model_config = {"extra": "allow"} 88 | 89 | 90 | class LineItemResponse(BaseModel): 91 | search_results: list[LineItem] 92 | 93 | 94 | class InsiderTrade(BaseModel): 95 | ticker: str 96 | issuer: str | None 97 | name: str | None 98 | title: str | None 99 | is_board_director: bool | None 100 | transaction_date: str | None 101 | transaction_shares: float | None 102 | transaction_price_per_share: float | None 103 | transaction_value: float | None 104 | shares_owned_before_transaction: float | None 105 | shares_owned_after_transaction: float | None 106 | security_title: str | None 107 | filing_date: str 108 | 109 | 110 | class InsiderTradeResponse(BaseModel): 111 | insider_trades: list[InsiderTrade] 112 | 113 | 114 | class CompanyNews(BaseModel): 115 | ticker: str 116 | title: str 117 | author: str 118 | source: str 119 | date: str 120 | url: str 121 | sentiment: str | None = None 122 | 123 | 124 | class CompanyNewsResponse(BaseModel): 125 | news: list[CompanyNews] 126 | 127 | 128 | class Position(BaseModel): 129 | cash: float = 0.0 130 | shares: int = 0 131 | ticker: str 132 | 133 | 134 | class Portfolio(BaseModel): 135 | positions: dict[str, Position] # ticker -> Position mapping 136 | total_cash: float = 0.0 137 | 138 | 139 | class AnalystSignal(BaseModel): 140 | signal: str | None = None 141 | confidence: float | None = None 142 | reasoning: dict | str | None = None 143 | max_position_size: float | None = None # For risk management signals 144 | 145 | 146 | class TickerAnalysis(BaseModel): 147 | ticker: str 148 | analyst_signals: dict[str, AnalystSignal] # agent_name -> signal mapping 149 | 150 | 151 | class AgentStateData(BaseModel): 152 | tickers: list[str] 153 | portfolio: Portfolio 154 | start_date: str 155 | end_date: str 156 | ticker_analyses: dict[str, TickerAnalysis] # ticker -> analysis mapping 157 | 158 | 159 | class AgentStateMetadata(BaseModel): 160 | show_reasoning: bool = False 161 | model_config = {"extra": "allow"} 162 | -------------------------------------------------------------------------------- /backend/AI/graph/state.py: -------------------------------------------------------------------------------- 1 | from typing_extensions import Annotated, Sequence, TypedDict 2 | 3 | import operator 4 | from langchain_core.messages import BaseMessage 5 | 6 | 7 | import json 8 | 9 | 10 | def merge_dicts(a: dict[str, any], b: dict[str, any]) -> dict[str, any]: 11 | return {**a, **b} 12 | 13 | 14 | # Define agent state 15 | class AgentState(TypedDict): 16 | messages: Annotated[Sequence[BaseMessage], operator.add] 17 | data: Annotated[dict[str, any], merge_dicts] 18 | metadata: Annotated[dict[str, any], merge_dicts] 19 | 20 | 21 | def show_agent_reasoning(output, agent_name): 22 | print(f"\n{'=' * 10} {agent_name.center(28)} {'=' * 10}") 23 | 24 | def convert_to_serializable(obj): 25 | if hasattr(obj, "to_dict"): # Handle Pandas Series/DataFrame 26 | return obj.to_dict() 27 | elif hasattr(obj, "__dict__"): # Handle custom objects 28 | return obj.__dict__ 29 | elif isinstance(obj, (int, float, bool, str)): 30 | return obj 31 | elif isinstance(obj, (list, tuple)): 32 | return [convert_to_serializable(item) for item in obj] 33 | elif isinstance(obj, dict): 34 | return {key: convert_to_serializable(value) for key, value in obj.items()} 35 | else: 36 | return str(obj) # Fallback to string representation 37 | 38 | if isinstance(output, (dict, list)): 39 | # Convert the output to JSON-serializable format 40 | serializable_output = convert_to_serializable(output) 41 | print(json.dumps(serializable_output, indent=2)) 42 | else: 43 | try: 44 | # Parse the string as JSON and pretty print it 45 | parsed_output = json.loads(output) 46 | print(json.dumps(parsed_output, indent=2)) 47 | except json.JSONDecodeError: 48 | # Fallback to original string if not valid JSON 49 | print(output) 50 | 51 | print("=" * 48) 52 | -------------------------------------------------------------------------------- /backend/AI/llm/models.py: -------------------------------------------------------------------------------- 1 | import os 2 | from langchain_deepseek import ChatDeepSeek 3 | from langchain_openai import ChatOpenAI 4 | from langchain_ollama import ChatOllama 5 | from enum import Enum 6 | from pydantic import BaseModel 7 | from typing import Tuple, List, Dict, Any, Optional 8 | from config import settings 9 | 10 | class ModelProvider(str, Enum): 11 | """Enum for supported LLM providers""" 12 | DEEPSEEK = "DeepSeek" 13 | OPENAI = "OpenAI" 14 | OLLAMA = "Ollama" 15 | 16 | 17 | 18 | class LLMModel(BaseModel): 19 | """Represents an LLM model configuration""" 20 | display_name: str 21 | model_name: str 22 | provider: ModelProvider 23 | 24 | def to_choice_tuple(self) -> Tuple[str, str, str]: 25 | """Convert to format needed for questionary choices""" 26 | return (self.display_name, self.model_name, self.provider.value) 27 | 28 | def has_json_mode(self) -> bool: 29 | """Check if the model supports JSON mode""" 30 | if self.is_deepseek() or self.is_gemini(): 31 | return False 32 | # Only certain Ollama models support JSON mode 33 | if self.is_ollama(): 34 | return "llama3" in self.model_name or "neural-chat" in self.model_name 35 | return True 36 | 37 | def is_deepseek(self) -> bool: 38 | """Check if the model is a DeepSeek model""" 39 | return self.model_name.startswith("deepseek") 40 | 41 | def is_ollama(self) -> bool: 42 | """Check if the model is an Ollama model""" 43 | return self.provider == ModelProvider.OLLAMA 44 | 45 | 46 | # Define available models 47 | AVAILABLE_MODELS = [ 48 | LLMModel( 49 | display_name="[deepseek] deepseek-r1", 50 | model_name="deepseek-reasoner", 51 | provider=ModelProvider.DEEPSEEK 52 | ), 53 | LLMModel( 54 | display_name="[deepseek] deepseek-v3", 55 | model_name="deepseek-chat", 56 | provider=ModelProvider.DEEPSEEK 57 | ), 58 | LLMModel( 59 | display_name="[openai] gpt-4.5", 60 | model_name="gpt-4.5-preview", 61 | provider=ModelProvider.OPENAI 62 | ), 63 | LLMModel( 64 | display_name="[openai] gpt-4o", 65 | model_name="gpt-4o", 66 | provider=ModelProvider.OPENAI 67 | ), 68 | LLMModel( 69 | display_name="[openai] o1", 70 | model_name="o1", 71 | provider=ModelProvider.OPENAI 72 | ), 73 | LLMModel( 74 | display_name="[openai] o3-mini", 75 | model_name="o3-mini", 76 | provider=ModelProvider.OPENAI 77 | ), 78 | ] 79 | 80 | # Define Ollama models separately 81 | OLLAMA_MODELS = [ 82 | LLMModel( 83 | display_name="[ollama] gemma3 (4B)", 84 | model_name="gemma3:4b", 85 | provider=ModelProvider.OLLAMA 86 | ), 87 | LLMModel( 88 | display_name="[ollama] qwen2.5 (7B)", 89 | model_name="qwen2.5", 90 | provider=ModelProvider.OLLAMA 91 | ), 92 | LLMModel( 93 | display_name="[ollama] llama3.1 (8B)", 94 | model_name="llama3.1:latest", 95 | provider=ModelProvider.OLLAMA 96 | ), 97 | LLMModel( 98 | display_name="[ollama] gemma3 (12B)", 99 | model_name="gemma3:12b", 100 | provider=ModelProvider.OLLAMA 101 | ), 102 | LLMModel( 103 | display_name="[ollama] mistral-small3.1 (24B)", 104 | model_name="mistral-small3.1", 105 | provider=ModelProvider.OLLAMA 106 | ), 107 | LLMModel( 108 | display_name="[ollama] gemma3 (27B)", 109 | model_name="gemma3:27b", 110 | provider=ModelProvider.OLLAMA 111 | ), 112 | LLMModel( 113 | display_name="[ollama] qwen2.5 (32B)", 114 | model_name="qwen2.5:32b", 115 | provider=ModelProvider.OLLAMA 116 | ), 117 | LLMModel( 118 | display_name="[ollama] llama-3.3 (70B)", 119 | model_name="llama3.3:70b-instruct-q4_0", 120 | provider=ModelProvider.OLLAMA 121 | ), 122 | ] 123 | 124 | # Create LLM_ORDER in the format expected by the UI 125 | LLM_ORDER = [model.to_choice_tuple() for model in AVAILABLE_MODELS] 126 | 127 | # Create Ollama LLM_ORDER separately 128 | OLLAMA_LLM_ORDER = [model.to_choice_tuple() for model in OLLAMA_MODELS] 129 | 130 | def get_model_info(model_name: str) -> LLMModel | None: 131 | """Get model information by model_name""" 132 | all_models = AVAILABLE_MODELS + OLLAMA_MODELS 133 | return next((model for model in all_models if model.model_name == model_name), None) 134 | 135 | def get_model(model_name: str, model_provider: ModelProvider) -> ChatOpenAI | ChatOllama | None: 136 | if model_provider == ModelProvider.OPENAI: 137 | # Get and validate API key 138 | api_key = settings.OPENAI_API_KEY 139 | if not api_key: 140 | # Print error to console 141 | print(f"API Key Error: Please make sure OPENAI_API_KEY is set in your .env file.") 142 | raise ValueError("OpenAI API key not found. Please make sure OPENAI_API_KEY is set in your .env file.") 143 | base_url = settings.OPENAI_BASE_URL 144 | return ChatOpenAI(model=model_name, api_key=api_key, base_url=base_url) 145 | elif model_provider == ModelProvider.DEEPSEEK: 146 | api_key = settings.DEEPSEEK_API_KEY 147 | if not api_key: 148 | print(f"API Key Error: Please make sure DEEPSEEK_API_KEY is set in your .env file.") 149 | raise ValueError("DeepSeek API key not found. Please make sure DEEPSEEK_API_KEY is set in your .env file.") 150 | return ChatDeepSeek(model=model_name, api_key=api_key) 151 | elif model_provider == ModelProvider.OLLAMA: 152 | # For Ollama, we use a base URL instead of an API key 153 | base_url = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434") 154 | return ChatOllama( 155 | model=model_name, 156 | base_url=base_url, 157 | ) -------------------------------------------------------------------------------- /backend/AI/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # This file can be empty 2 | -------------------------------------------------------------------------------- /backend/AI/utils/analysts.py: -------------------------------------------------------------------------------- 1 | """Constants and utilities related to analysts configuration.""" 2 | 3 | from AI.agents.ben_graham import ben_graham_agent 4 | from AI.agents.bill_ackman import bill_ackman_agent 5 | from AI.agents.cathie_wood import cathie_wood_agent 6 | from AI.agents.charlie_munger import charlie_munger_agent 7 | from AI.agents.fundamentals import fundamentals_agent 8 | from AI.agents.michael_burry import michael_burry_agent 9 | from AI.agents.phil_fisher import phil_fisher_agent 10 | from AI.agents.peter_lynch import peter_lynch_agent 11 | from AI.agents.sentiment import sentiment_agent 12 | from AI.agents.stanley_druckenmiller import stanley_druckenmiller_agent 13 | from AI.agents.technicals import technical_analyst_agent 14 | from AI.agents.valuation import valuation_agent 15 | from AI.agents.warren_buffett import warren_buffett_agent 16 | 17 | # Define analyst configuration - single source of truth 18 | ANALYST_CONFIG = { 19 | "ben_graham": { 20 | "display_name": "Ben Graham", 21 | "agent_func": ben_graham_agent, 22 | "order": 0, 23 | }, 24 | "bill_ackman": { 25 | "display_name": "Bill Ackman", 26 | "agent_func": bill_ackman_agent, 27 | "order": 1, 28 | }, 29 | "cathie_wood": { 30 | "display_name": "Cathie Wood", 31 | "agent_func": cathie_wood_agent, 32 | "order": 2, 33 | }, 34 | "charlie_munger": { 35 | "display_name": "Charlie Munger", 36 | "agent_func": charlie_munger_agent, 37 | "order": 3, 38 | }, 39 | "michael_burry": { 40 | "display_name": "Michael Burry", 41 | "agent_func": michael_burry_agent, 42 | "order": 4, 43 | }, 44 | "peter_lynch": { 45 | "display_name": "Peter Lynch", 46 | "agent_func": peter_lynch_agent, 47 | "order": 5, 48 | }, 49 | "phil_fisher": { 50 | "display_name": "Phil Fisher", 51 | "agent_func": phil_fisher_agent, 52 | "order": 6, 53 | }, 54 | "stanley_druckenmiller": { 55 | "display_name": "Stanley Druckenmiller", 56 | "agent_func": stanley_druckenmiller_agent, 57 | "order": 7, 58 | }, 59 | "warren_buffett": { 60 | "display_name": "Warren Buffett", 61 | "agent_func": warren_buffett_agent, 62 | "order": 8, 63 | }, 64 | "technical_analyst": { 65 | "display_name": "Technical Analyst", 66 | "agent_func": technical_analyst_agent, 67 | "order": 9, 68 | }, 69 | "fundamentals_analyst": { 70 | "display_name": "Fundamentals Analyst", 71 | "agent_func": fundamentals_agent, 72 | "order": 10, 73 | }, 74 | "sentiment_analyst": { 75 | "display_name": "Sentiment Analyst", 76 | "agent_func": sentiment_agent, 77 | "order": 11, 78 | }, 79 | "valuation_analyst": { 80 | "display_name": "Valuation Analyst", 81 | "agent_func": valuation_agent, 82 | "order": 12, 83 | }, 84 | } 85 | 86 | # Derive ANALYST_ORDER from ANALYST_CONFIG for backwards compatibility 87 | ANALYST_ORDER = [(config["display_name"], key) for key, config in sorted(ANALYST_CONFIG.items(), key=lambda x: x[1]["order"])] 88 | 89 | 90 | def get_analyst_nodes(): 91 | """Get the mapping of analyst keys to their (node_name, agent_func) tuples.""" 92 | return {key: (f"{key}_agent", config["agent_func"]) for key, config in ANALYST_CONFIG.items()} 93 | -------------------------------------------------------------------------------- /backend/AI/utils/llm.py: -------------------------------------------------------------------------------- 1 | """Helper functions for LLM""" 2 | 3 | import json 4 | from typing import TypeVar, Type, Optional, Any 5 | from pydantic import BaseModel 6 | from AI.utils.progress import progress 7 | 8 | T = TypeVar('T', bound=BaseModel) 9 | 10 | def call_llm( 11 | prompt: Any, 12 | model_name: str, 13 | model_provider: str, 14 | pydantic_model: Type[T], 15 | agent_name: Optional[str] = None, 16 | max_retries: int = 3, 17 | default_factory = None 18 | ) -> T: 19 | """ 20 | Makes an LLM call with retry logic, handling both JSON supported and non-JSON supported models. 21 | 22 | Args: 23 | prompt: The prompt to send to the LLM 24 | model_name: Name of the model to use 25 | model_provider: Provider of the model 26 | pydantic_model: The Pydantic model class to structure the output 27 | agent_name: Optional name of the agent for progress updates 28 | max_retries: Maximum number of retries (default: 3) 29 | default_factory: Optional factory function to create default response on failure 30 | 31 | Returns: 32 | An instance of the specified Pydantic model 33 | """ 34 | from AI.llm.models import get_model, get_model_info 35 | 36 | model_info = get_model_info(model_name) 37 | llm = get_model(model_name, model_provider) 38 | 39 | # For non-JSON support models, we can use structured output 40 | if not (model_info and not model_info.has_json_mode()): 41 | llm = llm.with_structured_output( 42 | pydantic_model, 43 | method="json_mode", 44 | ) 45 | 46 | # Call the LLM with retries 47 | for attempt in range(max_retries): 48 | try: 49 | # Call the LLM 50 | result = llm.invoke(prompt) 51 | 52 | # For non-JSON support models, we need to extract and parse the JSON manually 53 | if model_info and not model_info.has_json_mode(): 54 | parsed_result = extract_json_from_response(result.content) 55 | if parsed_result: 56 | return pydantic_model(**parsed_result) 57 | else: 58 | return result 59 | 60 | except Exception as e: 61 | if agent_name: 62 | progress.update_status(agent_name, None, f"Error - retry {attempt + 1}/{max_retries}") 63 | 64 | if attempt == max_retries - 1: 65 | print(f"Error in LLM call after {max_retries} attempts: {e}") 66 | # Use default_factory if provided, otherwise create a basic default 67 | if default_factory: 68 | return default_factory() 69 | return create_default_response(pydantic_model) 70 | 71 | # This should never be reached due to the retry logic above 72 | return create_default_response(pydantic_model) 73 | 74 | def create_default_response(model_class: Type[T]) -> T: 75 | """Creates a safe default response based on the model's fields.""" 76 | default_values = {} 77 | for field_name, field in model_class.model_fields.items(): 78 | if field.annotation == str: 79 | default_values[field_name] = "Error in analysis, using default" 80 | elif field.annotation == float: 81 | default_values[field_name] = 0.0 82 | elif field.annotation == int: 83 | default_values[field_name] = 0 84 | elif hasattr(field.annotation, "__origin__") and field.annotation.__origin__ == dict: 85 | default_values[field_name] = {} 86 | else: 87 | # For other types (like Literal), try to use the first allowed value 88 | if hasattr(field.annotation, "__args__"): 89 | default_values[field_name] = field.annotation.__args__[0] 90 | else: 91 | default_values[field_name] = None 92 | 93 | return model_class(**default_values) 94 | 95 | def extract_json_from_response(content: str) -> Optional[dict]: 96 | """Extracts JSON from markdown-formatted response.""" 97 | try: 98 | json_start = content.find("```json") 99 | if json_start != -1: 100 | json_text = content[json_start + 7:] # Skip past ```json 101 | json_end = json_text.find("```") 102 | if json_end != -1: 103 | json_text = json_text[:json_end].strip() 104 | return json.loads(json_text) 105 | except Exception as e: 106 | print(f"Error extracting JSON from response: {e}") 107 | return None 108 | -------------------------------------------------------------------------------- /backend/AI/utils/progress.py: -------------------------------------------------------------------------------- 1 | from rich.console import Console 2 | from rich.live import Live 3 | from rich.table import Table 4 | from rich.style import Style 5 | from rich.text import Text 6 | from typing import Dict, Optional 7 | from datetime import datetime 8 | from loguru import logger 9 | 10 | console = Console() 11 | 12 | 13 | class AgentProgress: 14 | """管理多个代理的进度跟踪""" 15 | 16 | def __init__(self): 17 | self.agent_status: Dict[str, Dict[str, str]] = {} 18 | self.table = Table(show_header=False, box=None, padding=(0, 1)) 19 | self.live = Live(self.table, console=console, refresh_per_second=4) 20 | self.started = False 21 | 22 | def start(self): 23 | """开始进度显示""" 24 | if not self.started: 25 | self.live.start() 26 | self.started = True 27 | logger.info("开始显示进度") 28 | 29 | def stop(self): 30 | """停止进度显示""" 31 | if self.started: 32 | self.live.stop() 33 | self.started = False 34 | logger.info("停止显示进度") 35 | 36 | def update_status(self, agent_name: str, ticker: Optional[str] = None, status: str = ""): 37 | """更新代理状态""" 38 | if agent_name not in self.agent_status: 39 | self.agent_status[agent_name] = {"status": "", "ticker": None} 40 | 41 | if ticker: 42 | self.agent_status[agent_name]["ticker"] = ticker 43 | if status: 44 | self.agent_status[agent_name]["status"] = status 45 | logger.info(f"代理 {agent_name} 状态更新: {status}") 46 | 47 | self._refresh_display() 48 | 49 | def _refresh_display(self): 50 | """刷新进度显示""" 51 | self.table.columns.clear() 52 | self.table.add_column(width=100) 53 | 54 | # 对代理进行排序，风险管理和投资组合管理放在底部 55 | def sort_key(item): 56 | agent_name = item[0] 57 | if "risk_management" in agent_name: 58 | return (2, agent_name) 59 | elif "portfolio_management" in agent_name: 60 | return (3, agent_name) 61 | else: 62 | return (1, agent_name) 63 | 64 | for agent_name, info in sorted(self.agent_status.items(), key=sort_key): 65 | status = info["status"] 66 | ticker = info["ticker"] 67 | 68 | # 创建带有适当样式的状态文本 69 | if status.lower() == "done": 70 | style = Style(color="green", bold=True) 71 | symbol = "✓" 72 | elif status.lower() == "error": 73 | style = Style(color="red", bold=True) 74 | symbol = "✗" 75 | else: 76 | style = Style(color="yellow") 77 | symbol = "⋯" 78 | 79 | agent_display = agent_name.replace("_agent", "").replace("_", " ").title() 80 | status_text = Text() 81 | status_text.append(f"{symbol} ", style=style) 82 | status_text.append(f"{agent_display:<20}", style=Style(bold=True)) 83 | 84 | if ticker: 85 | status_text.append(f"[{ticker}] ", style=Style(color="cyan")) 86 | status_text.append(status, style=style) 87 | 88 | self.table.add_row(status_text) 89 | 90 | 91 | # 创建全局实例 92 | progress = AgentProgress() 93 | -------------------------------------------------------------------------------- /backend/AI/utils/visualize.py: -------------------------------------------------------------------------------- 1 | from langgraph.graph.state import CompiledGraph 2 | from langchain_core.runnables.graph import MermaidDrawMethod 3 | 4 | 5 | def save_graph_as_png(app: CompiledGraph, output_file_path) -> None: 6 | png_image = app.get_graph().draw_mermaid_png(draw_method=MermaidDrawMethod.API) 7 | file_path = output_file_path if len(output_file_path) > 0 else "graph.png" 8 | with open(file_path, "wb") as f: 9 | f.write(png_image) -------------------------------------------------------------------------------- /backend/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | 新致量化策略 Backend 3 | """ -------------------------------------------------------------------------------- /backend/config.py: -------------------------------------------------------------------------------- 1 | from pydantic_settings import BaseSettings 2 | from functools import lru_cache 3 | 4 | class Settings(BaseSettings): 5 | # Tushare API配置 6 | TUSHARE_TOKEN: str = "你的tushare token" 7 | 8 | # Deepseek API配置 9 | DEEPSEEK_API_KEY: str 10 | 11 | OPENAI_API_KEY: str 12 | OPENAI_BASE_URL: str 13 | 14 | # 应用配置 15 | PROJECT_NAME: str = "新致量化策略" 16 | API_V1_STR: str = "/api/v1" 17 | 18 | # 数据库配置 19 | DATABASE_URL: str | None = None 20 | 21 | # 日志配置 22 | LOG_LEVEL: str = "INFO" 23 | LOG_FILE_PATH: str = "logs/app.log" 24 | 25 | # 安全配置 26 | SECRET_KEY: str = "your_secret_key_here" 27 | ALGORITHM: str = "HS256" 28 | ACCESS_TOKEN_EXPIRE_MINUTES: int = 30 29 | 30 | class Config: 31 | env_file = ".env" 32 | case_sensitive = True 33 | 34 | @lru_cache() 35 | def get_settings(): 36 | return Settings() 37 | 38 | settings = get_settings() 39 | 40 | # 期货交易乘数配置 41 | FUTURES_MULTIPLIER = { 42 | # 商品期货 43 | 'M': 10, # 豆粕 44 | 'Y': 10, # 豆油 45 | 'P': 10, # 棕榈油 46 | 'C': 10, # 玉米 47 | 'A': 10, # 豆一 48 | 'RB': 10, # 螺纹钢 49 | 'I': 100, # 铁矿石 50 | 'J': 100, # 焦炭 51 | 'JM': 60, # 焦煤 52 | 'CU': 5, # 铜 53 | 'AL': 5, # 铝 54 | 'ZN': 5, # 锌 55 | 'PB': 5, # 铅 56 | 'AU': 1000, # 黄金 57 | 'AG': 15, # 白银 58 | 'RU': 10, # 橡胶 59 | 'FU': 10, # 燃油 60 | 'TA': 5, # PTA 61 | 'MA': 10, # 甲醇 62 | 'PP': 5, # 聚丙烯 63 | 'L': 5, # 塑料 64 | 'V': 5, # PVC 65 | 66 | # 金融期货 67 | 'IF': 300, # 沪深300 68 | 'IC': 200, # 中证500 69 | 'IH': 300, # 上证50 70 | 'T': 10000, # 国债 71 | 'TF': 10000, # 5年国债 72 | } 73 | 74 | def get_multiplier(symbol: str) -> int: 75 | """ 76 | 获取期货品种的交易乘数 77 | :param symbol: 期货代码，例如 'M2401' 78 | :return: 交易乘数，如果找不到则返回1 79 | """ 80 | # 提取品种代码（去掉月份） 81 | # 只提取第一个字母或连续的字母作为品种代码 82 | import re 83 | product = re.match(r'([A-Za-z]+)', symbol.upper()) 84 | product = product.group(1) if product else "" 85 | return FUTURES_MULTIPLIER.get(product, 1) 86 | 87 | def is_futures(symbol: str) -> bool: 88 | """ 89 | 判断是否是期货品种 90 | :param symbol: 完整代码，例如 'futures-M2401' 91 | :return: 是否是期货 92 | """ 93 | if not symbol: 94 | return False 95 | parts = symbol.split('-') 96 | return len(parts) > 1 and parts[0].lower() == 'futures' -------------------------------------------------------------------------------- /backend/init_db.py: -------------------------------------------------------------------------------- 1 | from models.soybean import Base 2 | from models.trading_strategy import Base as TradingStrategyBase 3 | from config import settings 4 | from sqlalchemy import create_engine 5 | 6 | def init_db(): 7 | """初始化数据库表""" 8 | engine = create_engine(settings.DATABASE_URL or "sqlite:///./trading.db") 9 | Base.metadata.create_all(bind=engine) 10 | TradingStrategyBase.metadata.create_all(bind=engine) 11 | print("数据库表初始化完成") 12 | 13 | if __name__ == "__main__": 14 | init_db() -------------------------------------------------------------------------------- /backend/main.py: -------------------------------------------------------------------------------- 1 | from fastapi import FastAPI 2 | from fastapi.middleware.cors import CORSMiddleware 3 | import sys 4 | import os 5 | 6 | # 添加项目根目录到Python路径 7 | sys.path.append(os.path.dirname(os.path.abspath(__file__))) 8 | 9 | from routers import market_data, trading, fundamental, core_factor, arbitrage, trend_follow, dual_ma, obv_adx_ema, news, ai, signals, account, grid, support_resistance, soybean, stockfutures, holding_analysis 10 | from config import settings 11 | from utils.logger import logger 12 | 13 | app = FastAPI( 14 | title=settings.PROJECT_NAME, 15 | openapi_url=f"{settings.API_V1_STR}/openapi.json" 16 | ) 17 | 18 | # 配置CORS 19 | app.add_middleware( 20 | CORSMiddleware, 21 | allow_origins=["http://localhost:3000"], # 允许前端开发服务器访问 22 | allow_credentials=True, 23 | allow_methods=["*"], # 允许所有HTTP方法 24 | allow_headers=["*"], # 允许所有请求头 25 | ) 26 | logger.info("CORS中间件配置完成") 27 | 28 | # 注册路由 29 | app.include_router( 30 | market_data.router, 31 | prefix=f"{settings.API_V1_STR}/market", 32 | tags=["market"] 33 | ) 34 | logger.info("市场数据路由注册完成") 35 | 36 | app.include_router( 37 | trading.router, 38 | prefix=f"{settings.API_V1_STR}/trading", 39 | tags=["trading"] 40 | ) 41 | logger.info("交易策略路由注册完成") 42 | 43 | app.include_router( 44 | fundamental.router, 45 | prefix=f"{settings.API_V1_STR}/fundamental", 46 | tags=["fundamental"] 47 | ) 48 | logger.info("基本面分析路由注册完成") 49 | 50 | app.include_router( 51 | core_factor.router, 52 | prefix=f"{settings.API_V1_STR}/core-factor", 53 | tags=["core_factor"] 54 | ) 55 | logger.info("核心驱动因子分析路由注册完成") 56 | 57 | app.include_router( 58 | arbitrage.router, 59 | prefix=f"{settings.API_V1_STR}/arbitrage", 60 | tags=["arbitrage"] 61 | ) 62 | logger.info("套利策略路由注册完成") 63 | 64 | app.include_router( 65 | trend_follow.router, 66 | prefix=f"{settings.API_V1_STR}/trend_follow", 67 | tags=["trend_follow"] 68 | ) 69 | logger.info("趋势跟随策略路由注册完成") 70 | 71 | app.include_router( 72 | dual_ma.router, 73 | prefix=f"{settings.API_V1_STR}/dual_ma", 74 | tags=["dual_ma"] 75 | ) 76 | logger.info("双均线策略路由注册完成") 77 | 78 | app.include_router( 79 | grid.router, 80 | prefix=f"{settings.API_V1_STR}/grid", 81 | tags=["grid"] 82 | ) 83 | logger.info("网格策略路由注册完成") 84 | 85 | app.include_router( 86 | obv_adx_ema.router, 87 | prefix=f"{settings.API_V1_STR}/obv_adx_ema", 88 | tags=["obv_adx_ema"] 89 | ) 90 | logger.info("OBV、ADX与EMA组合策略路由注册完成") 91 | 92 | app.include_router( 93 | support_resistance.router, 94 | prefix=f"{settings.API_V1_STR}/support_resistance", 95 | tags=["support_resistance"] 96 | ) 97 | logger.info("支撑阻力策略路由注册完成") 98 | 99 | app.include_router( 100 | news.router, 101 | prefix=f"{settings.API_V1_STR}/news", 102 | tags=["news"] 103 | ) 104 | logger.info("新闻分析路由注册完成") 105 | 106 | app.include_router( 107 | ai.router, 108 | prefix=f"{settings.API_V1_STR}/ai", 109 | tags=["ai"] 110 | ) 111 | logger.info("AI分析路由注册完成") 112 | 113 | app.include_router( 114 | signals.router, 115 | prefix=f"{settings.API_V1_STR}", 116 | tags=["signals"] 117 | ) 118 | logger.info("信号路由注册完成") 119 | 120 | app.include_router( 121 | account.router, 122 | prefix=f"{settings.API_V1_STR}/account", 123 | tags=["account"] 124 | ) 125 | logger.info("账户路由注册完成") 126 | 127 | app.include_router( 128 | soybean.router, 129 | prefix=f"{settings.API_V1_STR}/soybean", 130 | tags=["soybean"] 131 | ) 132 | logger.info("大豆进口分析路由注册完成") 133 | 134 | app.include_router( 135 | stockfutures.router, 136 | prefix=f"{settings.API_V1_STR}/stockfutures", 137 | tags=["stockfutures"] 138 | ) 139 | logger.info("期股联动分析路由注册完成") 140 | 141 | app.include_router( 142 | holding_analysis.router, 143 | prefix=f"{settings.API_V1_STR}/holding", 144 | tags=["holding"] 145 | ) 146 | logger.info("持仓变化分析路由注册完成") 147 | 148 | @app.on_event("startup") 149 | async def startup_event(): 150 | logger.info("应用启动") 151 | logger.info(f"项目名称: {settings.PROJECT_NAME}") 152 | logger.info(f"API版本: {settings.API_V1_STR}") 153 | 154 | # 检查 Tushare Token 155 | if not settings.TUSHARE_TOKEN: 156 | logger.error("Tushare token not configured") 157 | sys.exit(1) 158 | 159 | @app.on_event("shutdown") 160 | async def shutdown_event(): 161 | logger.info("应用关闭") 162 | 163 | @app.get("/") 164 | async def root(): 165 | logger.debug("收到根路径请求") 166 | return {"message": "Welcome to 新致量化策略 API"} -------------------------------------------------------------------------------- /backend/models/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Data Models 3 | """ -------------------------------------------------------------------------------- /backend/models/account.py: -------------------------------------------------------------------------------- 1 | from sqlalchemy import Column, Integer, String, DateTime, Float, func 2 | from sqlalchemy.ext.declarative import declarative_base 3 | from pydantic import BaseModel 4 | from datetime import datetime 5 | from typing import Optional 6 | 7 | Base = declarative_base() 8 | 9 | class AccountDB(Base): 10 | __tablename__ = "accounts" 11 | 12 | id = Column(Integer, primary_key=True, index=True) 13 | initial_balance = Column(Float, nullable=False, default=1000000.0) # 初始资金 14 | current_balance = Column(Float, nullable=False, default=1000000.0) # 当前资产 15 | available_balance = Column(Float, nullable=False) 16 | total_profit = Column(Float, nullable=False, default=0.0) 17 | total_commission = Column(Float, nullable=False, default=0.0) # 总手续费 18 | position_cost = Column(Float, nullable=False, default=0.0) # 持仓成本 19 | position_quantity = Column(Integer, nullable=False, default=0) # 持仓数量 20 | created_at = Column(DateTime, default=func.current_timestamp()) 21 | updated_at = Column(DateTime, default=func.current_timestamp(), onupdate=func.current_timestamp()) 22 | 23 | class AccountBase(BaseModel): 24 | initial_balance: float 25 | current_balance: float 26 | available_balance: float 27 | total_profit: float 28 | total_commission: float 29 | position_cost: float = 0.0 # 持仓成本 30 | position_quantity: int = 0 # 持仓数量 31 | 32 | class AccountCreate(AccountBase): 33 | pass 34 | 35 | class Account(AccountBase): 36 | id: int 37 | created_at: datetime 38 | updated_at: datetime 39 | 40 | class Config: 41 | from_attributes = True -------------------------------------------------------------------------------- /backend/models/core_factor.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime, date 2 | from typing import Dict, Any, Optional 3 | from sqlalchemy import Column, Integer, Date, DateTime, JSON 4 | from sqlalchemy.ext.declarative import declarative_base 5 | from pydantic import BaseModel 6 | 7 | Base = declarative_base() 8 | 9 | class CoreFactorAnalysisDB(Base): 10 | __tablename__ = "core_factor_analysis" 11 | 12 | id = Column(Integer, primary_key=True, index=True) 13 | date = Column(Date, unique=True, index=True) 14 | inventory_cycle = Column(JSON) 15 | technical_signals = Column(JSON) 16 | price_anchors = Column(JSON) 17 | news_policy = Column(JSON) 18 | hog_market = Column(JSON) 19 | created_at = Column(DateTime, default=datetime.utcnow) 20 | updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow) 21 | 22 | class CoreFactorAnalysis(BaseModel): 23 | date: date 24 | inventory_cycle: Dict[str, Any] 25 | technical_signals: Dict[str, Any] 26 | price_anchors: Dict[str, Any] 27 | news_policy: Dict[str, Any] 28 | hog_market: Dict[str, Any] 29 | created_at: Optional[datetime] = None 30 | updated_at: Optional[datetime] = None 31 | 32 | class Config: 33 | orm_mode = True -------------------------------------------------------------------------------- /backend/models/fundamental.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | from typing import List, Dict, Any, Optional 3 | from datetime import date, datetime 4 | from sqlalchemy import Column, String, Date, DateTime, JSON 5 | from sqlalchemy.ext.declarative import declarative_base 6 | 7 | Base = declarative_base() 8 | 9 | class FundamentalAnalysisDB(Base): 10 | __tablename__ = "fundamental_analysis" 11 | 12 | date = Column(Date, primary_key=True) 13 | supply_demand = Column(JSON) 14 | seasonal = Column(JSON) 15 | weather = Column(JSON) 16 | crush_profit = Column(JSON) 17 | overall = Column(JSON) 18 | created_at = Column(DateTime, default=datetime.utcnow) 19 | updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow) 20 | 21 | class FundamentalAnalysis(BaseModel): 22 | """用于API响应的Pydantic模型""" 23 | date: date 24 | supply_demand: Dict[str, Any] 25 | seasonal: Dict[str, Any] 26 | weather: Dict[str, Any] 27 | crush_profit: Dict[str, Any] 28 | overall: Dict[str, Any] 29 | created_at: Optional[datetime] = None 30 | updated_at: Optional[datetime] = None -------------------------------------------------------------------------------- /backend/models/kline.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | from datetime import datetime 3 | from typing import List, Optional 4 | 5 | class KLineData(BaseModel): 6 | date: datetime 7 | symbol: str 8 | open: float 9 | high: float 10 | low: float 11 | close: float 12 | volume: float 13 | ema5: float 14 | ema20: float 15 | open_interest: float 16 | 17 | class SignalRequest(BaseModel): 18 | start_date: str 19 | end_date: str 20 | type: Optional[str] = None 21 | page: int = 1 22 | page_size: int = 10 23 | klines: List[KLineData] -------------------------------------------------------------------------------- /backend/models/market_data.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel, Field 2 | from typing import Optional, List, Union 3 | from datetime import date 4 | 5 | class FuturesData(BaseModel): 6 | ts_code: str 7 | trade_date: str 8 | pre_close: float 9 | pre_settle: float 10 | open: float 11 | high: float 12 | low: float 13 | close: float 14 | settle: float 15 | change1: float 16 | change2: float 17 | vol: float 18 | amount: float 19 | oi: float 20 | oi_chg: float 21 | contract: str 22 | price: float 23 | historicalPrices: List[dict] = Field(default_factory=list) 24 | volume: Optional[float] = None 25 | 26 | class ETFData(BaseModel): 27 | ts_code: str 28 | trade_date: str 29 | open: float 30 | high: float 31 | low: float 32 | close: float 33 | vol: float 34 | amount: float 35 | ma5: float 36 | ma8: float 37 | atr: float 38 | signal: Optional[str] = None # 'buy', 'sell', 'hold' 39 | stop_loss: Optional[float] = None 40 | take_profit: Optional[float] = None 41 | last_signal: Optional[str] = None # 上一个有效信号 42 | last_signal_date: Optional[str] = None # 信号触发时间 43 | last_signal_price: Optional[float] = None # 信号触发价格 44 | last_stop_loss: Optional[float] = None # 上一个止损价格 45 | last_take_profit: Optional[float] = None # 上一个止盈价格 46 | 47 | class OptionsData(BaseModel): 48 | ts_code: str 49 | name: str 50 | underlying: str 51 | exchange: str 52 | call_put: str 53 | exercise_price: float 54 | exercise_date: date 55 | list_date: date 56 | delist_date: Optional[date] 57 | 58 | class InventoryData(BaseModel): 59 | date: str 60 | value: float 61 | mom_change: float # 环比变化 62 | yoy_change: float # 同比变化 63 | data_type: str = Field(description="数据类型：'inventory' 或 'warehouse_receipts'") # 数据类型，区分库存和仓单 64 | 65 | class TechnicalIndicators(BaseModel): 66 | contract: str 67 | last_updated: str 68 | current_price: float 69 | price_targets: dict 70 | ema: dict 71 | macd: dict 72 | rsi: dict 73 | kdj: dict 74 | bollinger_bands: dict 75 | volume: dict 76 | 77 | class OptionsHedgeData(BaseModel): 78 | ts_code: str 79 | trade_date: str 80 | futures_price: float 81 | options_price: float 82 | delta: float 83 | gamma: float 84 | theta: float 85 | vega: float 86 | hedge_ratio: float 87 | pl: float 88 | cumulative_pl: float 89 | signal: str 90 | volatility: float 91 | risk_exposure: Optional[float] = None 92 | 93 | class OptionBasic(BaseModel): 94 | ts_code: str 95 | name: str 96 | exercise_price: float 97 | maturity_date: str 98 | call_put: str 99 | exchange: str = 'DCE' # 默认为大连商品交易所 100 | opt_code: Optional[str] = None # 标准期权代码 101 | underlying_code: Optional[str] = None # 标的代码 102 | 103 | class OptionDaily(BaseModel): 104 | ts_code: str 105 | trade_date: str 106 | exchange: str = 'DCE' # 默认为大连商品交易所 107 | pre_settle: Optional[float] = None 108 | pre_close: Optional[float] = None 109 | open: Optional[float] = None 110 | high: Optional[float] = None 111 | low: Optional[float] = None 112 | close: Optional[float] = None 113 | settle: Optional[float] = None 114 | vol: Optional[float] = None 115 | amount: Optional[float] = None 116 | oi: Optional[float] = None 117 | 118 | class CostComparisonData(BaseModel): 119 | date: str 120 | cost: float # 豆粕成本价 121 | futures_price: float # 主力合约价格 122 | price_diff: float # 价差 123 | price_ratio: float # 价格比 124 | 125 | class KlineData(BaseModel): 126 | trade_date: str 127 | open: float 128 | high: float 129 | low: float 130 | close: float 131 | vol: float 132 | 133 | class HistoricalBottom(BaseModel): 134 | start_date: str 135 | end_date: str 136 | duration: int 137 | bounce_amplitude: float 138 | lowest_price: float 139 | contract: str 140 | kline_data: Optional[List[KlineData]] 141 | 142 | class ContractStats(BaseModel): 143 | contract: str 144 | lowest_price: float 145 | highest_price: float 146 | price_range: float 147 | start_price: float 148 | end_price: float 149 | volatility_30d: float # 30日波动率 150 | quantile_coef: float # 分位系数 = 最低价/开始价格 151 | standardized_value: float # 标准化值 = (当前价-最低价)/(最高价-最低价) 152 | 153 | class PriceRangeAnalysis(BaseModel): 154 | bottom_price: float 155 | current_price: float 156 | bottom_range_start: float 157 | bottom_range_end: float 158 | bounce_success_rate: float 159 | avg_bounce_amplitude: float 160 | avg_bottom_duration: float 161 | historical_bottoms: List[HistoricalBottom] 162 | contract_stats: List[ContractStats] 163 | price_quartiles: dict = Field( # 价格分位数 164 | default_factory=lambda: { 165 | 'q1': 0.0, 166 | 'q2': 0.0, 167 | 'q3': 0.0 168 | } 169 | ) 170 | volatility_quartiles: dict = Field( # 波动率分位数 171 | default_factory=lambda: { 172 | 'q1': 0.0, 173 | 'q2': 0.0, 174 | 'q3': 0.0 175 | } 176 | ) 177 | cycle_analysis: dict = Field( # 周期性分析 178 | default_factory=lambda: { 179 | 'cycle_length': 4, # 周期长度（年） 180 | 'last_bottom_year': 2020, # 上一个周期底部年份 181 | 'next_bottom_year': 2024, # 预测下一个周期底部年份 182 | 'current_phase': 'late', # 当前所处周期阶段 early/mid/late 183 | } 184 | ) 185 | predicted_low: dict = Field( # 低点预测 186 | default_factory=lambda: { 187 | 'base': 0.0, # 基准预测值 188 | 'lower': 0.0, # 下限 189 | 'upper': 0.0, # 上限 190 | 'confidence': 0.0, # 预测置信度 191 | 'factors': { # 影响因子 192 | 'supply_pressure': 0.0, # 供应压力 193 | 'policy_risk': 0.0, # 政策风险 194 | 'basis_impact': 0.0, # 基差影响 195 | } 196 | } 197 | ) -------------------------------------------------------------------------------- /backend/models/news.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | from typing import Optional 3 | from datetime import datetime 4 | 5 | class FlashNews(BaseModel): 6 | """快讯数据模型""" 7 | id: Optional[int] = None 8 | datetime: datetime 9 | content: str 10 | analysis: Optional[str] = None 11 | remarks: Optional[str] = None 12 | 13 | class NewsArticle(BaseModel): 14 | """资讯数据模型""" 15 | id: Optional[int] = None 16 | datetime: datetime 17 | title: str 18 | content: str 19 | analysis: Optional[str] = None 20 | remarks: Optional[str] = None -------------------------------------------------------------------------------- /backend/models/position.py: -------------------------------------------------------------------------------- 1 | from sqlalchemy import Column, Integer, String, Float, DateTime, func 2 | from sqlalchemy.ext.declarative import declarative_base 3 | from pydantic import BaseModel 4 | from datetime import datetime 5 | from typing import Optional 6 | 7 | Base = declarative_base() 8 | 9 | class PositionDB(Base): 10 | __tablename__ = "positions" 11 | 12 | id = Column(Integer, primary_key=True, index=True) 13 | symbol = Column(String, nullable=False, index=True) 14 | price = Column(Float, nullable=False) # 持仓成本 15 | quantity = Column(Integer, nullable=False) # 持仓数量 16 | status = Column(String, nullable=False, default='open') # open/closed 17 | created_at = Column(DateTime, default=func.current_timestamp()) 18 | updated_at = Column(DateTime, default=func.current_timestamp(), onupdate=func.current_timestamp()) 19 | 20 | class PositionBase(BaseModel): 21 | symbol: str 22 | price: float 23 | quantity: int 24 | status: str = 'open' 25 | 26 | class PositionCreate(PositionBase): 27 | pass 28 | 29 | class Position(PositionBase): 30 | id: int 31 | created_at: datetime 32 | updated_at: datetime 33 | 34 | class Config: 35 | from_attributes = True -------------------------------------------------------------------------------- /backend/models/signals.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel, Field 2 | from typing import Optional 3 | from datetime import datetime 4 | from enum import Enum 5 | import uuid 6 | 7 | class SignalType(str, Enum): 8 | BUY_OPEN = "BUY_OPEN" 9 | SELL_OPEN = "SELL_OPEN" 10 | BUY_CLOSE = "BUY_CLOSE" 11 | SELL_CLOSE = "SELL_CLOSE" 12 | 13 | class SignalStatus(str, Enum): 14 | OPEN = "open" 15 | CLOSED = "closed" 16 | PARTIAL_CLOSED = "partial_closed" 17 | 18 | class SignalBase(BaseModel): 19 | date: datetime 20 | symbol: str 21 | type: SignalType 22 | price: float 23 | quantity: int 24 | status: SignalStatus 25 | reason: str # 开平仓原因 26 | close_date: Optional[datetime] = None 27 | close_price: Optional[float] = None 28 | profit: float = 0.0 29 | 30 | class SignalCreate(SignalBase): 31 | pass 32 | 33 | class SignalUpdate(BaseModel): 34 | status: Optional[SignalStatus] = None 35 | close_date: Optional[datetime] = None 36 | close_price: Optional[float] = None 37 | profit: Optional[float] = None 38 | reason: Optional[str] = None # 平仓原因 39 | 40 | class Signal(SignalBase): 41 | id: str 42 | created_at: datetime 43 | updated_at: datetime 44 | 45 | class Config: 46 | from_attributes = True -------------------------------------------------------------------------------- /backend/models/soybean.py: -------------------------------------------------------------------------------- 1 | from datetime import date, datetime 2 | from typing import Dict, List, Optional 3 | from pydantic import BaseModel 4 | from sqlalchemy import Column, Integer, Float, String, Date, DateTime, JSON 5 | from sqlalchemy.ext.declarative import declarative_base 6 | 7 | Base = declarative_base() 8 | 9 | class PolicyEvent(BaseModel): 10 | """政策事件模型""" 11 | date: str 12 | event: str 13 | impact: str 14 | type: str 15 | 16 | class SoybeanImportDB(Base): 17 | """大豆进口数据库模型""" 18 | __tablename__ = "soybean_imports" 19 | 20 | id = Column(Integer, primary_key=True, index=True) 21 | date = Column(Date, nullable=False, index=True) 22 | # 装船数据 23 | current_shipment = Column(Float, nullable=False) # 当前装船量 24 | forecast_shipment = Column(Float, nullable=False) # 预计装船量 25 | forecast_next_shipment = Column(Float, nullable=False) # 下月预计装船量 26 | 27 | # 到港数据 28 | current_arrival = Column(Float, nullable=False) # 当月到港量 29 | next_arrival = Column(Float, nullable=False) # 下月到港预期 30 | current_month_arrival = Column(Float, nullable=False) # 当月实际到港量 31 | next_month_arrival = Column(Float, nullable=False) # 下月预计到港量 32 | 33 | port_details = Column(JSON, nullable=False) # 存储港口详细数据 34 | customs_details = Column(JSON, nullable=False) # 存储海关详细数据 35 | policy_events = Column(JSON, nullable=True) # 存储政策事件数据 36 | created_at = Column(DateTime, default=datetime.utcnow) 37 | updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow) 38 | 39 | class PortDetail(BaseModel): 40 | """港口详细数据模型""" 41 | port: str 42 | current: float 43 | next_month: float 44 | next_two_month: float 45 | 46 | class CustomsDetail(BaseModel): 47 | """海关详细数据模型""" 48 | customs: str 49 | current: float 50 | next_period: float 51 | next_month: float 52 | next_two_month: float 53 | 54 | class ComparisonData(BaseModel): 55 | """月度对比数据""" 56 | month: str 57 | value: float 58 | type: str 59 | 60 | class PortDistributionData(BaseModel): 61 | """港口分布数据""" 62 | port: str 63 | value: float 64 | type: str 65 | 66 | class SoybeanImport(BaseModel): 67 | """用于API响应的大豆进口数据模型""" 68 | date: str # YYYY-MM-DD 69 | 70 | # 装船数据 71 | current_shipment: float 72 | forecast_shipment: float 73 | forecast_next_shipment: float 74 | 75 | # 到港数据 76 | current_arrival: float 77 | next_arrival: float 78 | current_month_arrival: float 79 | next_month_arrival: float 80 | 81 | # 同环比数据 82 | current_shipment_yoy: float = 0.0 83 | current_shipment_mom: float = 0.0 84 | forecast_shipment_yoy: float = 0.0 85 | forecast_shipment_mom: float = 0.0 86 | current_arrival_yoy: float = 0.0 87 | current_arrival_mom: float = 0.0 88 | next_arrival_yoy: float = 0.0 89 | 90 | # 预期差异 91 | shipment_forecast_diff: float = 0.0 92 | arrival_forecast_diff: float = 0.0 93 | 94 | # 图表数据 95 | monthly_comparison: List[ComparisonData] = [] 96 | port_distribution: List[PortDistributionData] = [] 97 | 98 | # 详细数据 99 | port_details: List[PortDetail] 100 | customs_details: List[CustomsDetail] 101 | policy_events: List[PolicyEvent] = [] 102 | created_at: Optional[datetime] = None 103 | updated_at: Optional[datetime] = None 104 | 105 | class Config: 106 | from_attributes = True 107 | 108 | class PolicyEvent(BaseModel): 109 | """政策事件模型""" 110 | date: str 111 | event: str 112 | impact: str 113 | type: str -------------------------------------------------------------------------------- /backend/models/trading.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | from typing import List, Dict, Optional 3 | from datetime import date 4 | 5 | class StrategyDetails(BaseModel): 6 | entry_points: List[str] 7 | exit_points: List[str] 8 | risk_management: List[str] 9 | 10 | class OptionsStrategy(BaseModel): 11 | id: str 12 | title: str 13 | description: str 14 | risk_level: str 15 | expected_return: str 16 | time_horizon: str 17 | strategy_details: StrategyDetails 18 | 19 | class DailyStrategyAnalysis(BaseModel): 20 | date: date 21 | reasoning_content: str 22 | content: str 23 | created_at: Optional[date] = None 24 | updated_at: Optional[date] = None -------------------------------------------------------------------------------- /backend/models/trading_strategy.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime 2 | from sqlalchemy import Column, Integer, String, DateTime, JSON, Float 3 | from sqlalchemy.ext.declarative import declarative_base 4 | 5 | Base = declarative_base() 6 | 7 | class TradingStrategy(Base): 8 | """操盘策略数据库模型""" 9 | __tablename__ = "trading_strategies" 10 | 11 | id = Column(Integer, primary_key=True, index=True) 12 | contract = Column(String, nullable=False, index=True) # 合约代码 13 | strategy = Column(String, nullable=False) # 策略内容 14 | sr_levels = Column(JSON, nullable=True) # 支撑阻力位数据 15 | created_at = Column(DateTime, default=datetime.utcnow) 16 | updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow) -------------------------------------------------------------------------------- /backend/requirements.txt: -------------------------------------------------------------------------------- 1 | fastapi 2 | uvicorn 3 | tushare 4 | pandas 5 | pydantic 6 | pydantic-settings 7 | python-dotenv 8 | sqlalchemy 9 | python-jose 10 | passlib 11 | python-multipart 12 | scikit-learn 13 | tensorflow 14 | loguru 15 | akshare 16 | httpx 17 | openai 18 | beautifulsoup4 19 | aiohttp 20 | langchain_core 21 | langgraph 22 | questionary 23 | langchain_openai 24 | langchain_ollama 25 | langchain_deepseek 26 | colorama 27 | matplotlib -------------------------------------------------------------------------------- /backend/routers/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | API Routers 3 | """ -------------------------------------------------------------------------------- /backend/routers/account.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, Depends 2 | from services.account import AccountService 3 | from models.account import Account 4 | 5 | router = APIRouter() 6 | 7 | @router.get("/account", response_model=Account) 8 | async def get_account(): 9 | """获取账户信息""" 10 | account_service = AccountService() 11 | return account_service.get_account() -------------------------------------------------------------------------------- /backend/routers/ai.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException 2 | from pydantic import BaseModel 3 | from typing import List, Dict, Optional 4 | from datetime import datetime 5 | import sys 6 | import os 7 | from loguru import logger 8 | 9 | # 添加项目根目录到Python路径 10 | sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) 11 | 12 | from AI.AIService import run_hedge_fund 13 | from AI.backtester import Backtester 14 | 15 | router = APIRouter() 16 | 17 | class BacktestRequest(BaseModel): 18 | tickers: List[str] 19 | start_date: str 20 | end_date: str 21 | initial_capital: float = 100000.0 22 | portfolio: Dict[str, float] 23 | selected_analysts: Optional[List[str]] = [] 24 | model_name: str = "bot-20250329163710-8zcqm" 25 | model_provider: str = "OpenAI" 26 | 27 | @router.post("/backtest") 28 | async def run_backtest(request: BacktestRequest): 29 | """ 30 | 运行回测接口 31 | 32 | 参数: 33 | request: 回测请求对象，包含股票代码、日期范围、初始资金、投资组合等信息 34 | 35 | 返回: 36 | 包含分析结果和回测结果的字典 37 | """ 38 | logger.info(f"开始回测: 股票={request.tickers}, 开始日期={request.start_date}, 结束日期={request.end_date}") 39 | logger.info(f"回测参数: 初始资金={request.initial_capital}, 模型={request.model_name}, 提供商={request.model_provider}") 40 | 41 | try: 42 | # 运行对冲基金分析 43 | logger.info("开始运行对冲基金分析...") 44 | result = run_hedge_fund( 45 | tickers=request.tickers, 46 | start_date=request.start_date, 47 | end_date=request.end_date, 48 | portfolio=request.portfolio, 49 | selected_analysts=request.selected_analysts, 50 | model_name=request.model_name, 51 | model_provider=request.model_provider 52 | ) 53 | logger.info("对冲基金分析完成") 54 | 55 | # 初始化回测器 56 | logger.info("初始化回测器...") 57 | backtester = Backtester( 58 | agent=run_hedge_fund, 59 | tickers=request.tickers, 60 | start_date=request.start_date, 61 | end_date=request.end_date, 62 | initial_capital=request.initial_capital, 63 | model_name=request.model_name, 64 | model_provider=request.model_provider, 65 | selected_analysts=request.selected_analysts 66 | ) 67 | 68 | # 运行回测 69 | logger.info("开始运行回测...") 70 | backtest_results = backtester.run_backtest() 71 | logger.info("回测完成") 72 | 73 | logger.info(f"回测成功: 股票数量={len(request.tickers)}, 回测结果={backtest_results}") 74 | 75 | return { 76 | "analysis": result, 77 | "backtest": backtest_results 78 | } 79 | 80 | except Exception as e: 81 | logger.error(f"回测失败: {str(e)}") 82 | import traceback 83 | logger.error(f"异常堆栈: {traceback.format_exc()}") 84 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /backend/routers/core_factor.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException, Depends 2 | from datetime import datetime 3 | from typing import Dict, Any 4 | from services.core_factor import CoreFactorAnalyzer 5 | 6 | router = APIRouter() 7 | analyzer = CoreFactorAnalyzer() 8 | 9 | @router.get("/{date}") 10 | async def get_core_factor_analysis(date: str) -> Dict[str, Any]: 11 | """获取指定日期的核心驱动因子分析数据""" 12 | try: 13 | return await analyzer.get_core_factor_analysis(date) 14 | except Exception as e: 15 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /backend/routers/dual_ma.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException 2 | from pathlib import Path 3 | import pandas as pd 4 | import numpy as np 5 | import talib 6 | from utils.logger import logger 7 | from datetime import datetime 8 | from typing import List, Dict, Union 9 | from strategies.dual_ma_strategy import DualMAStrategy 10 | from pydantic import BaseModel 11 | 12 | class BacktestRequest(BaseModel): 13 | use_atr_tp: bool = False 14 | data_period: str = 'weekly' # 新增数据周期参数 15 | 16 | router = APIRouter() 17 | 18 | def load_and_process_data(file_path): 19 | try: 20 | logger.info(f"开始加载数据文件: {file_path}") 21 | 22 | if not file_path.exists(): 23 | error_msg = f"数据文件不存在: {file_path}" 24 | logger.error(error_msg) 25 | raise HTTPException(status_code=404, detail=error_msg) 26 | 27 | df = pd.read_csv(file_path) 28 | logger.info(f"成功读取CSV文件，总数据行数: {len(df)}") 29 | 30 | logger.debug("开始处理时间列") 31 | # 根据文件名判断数据周期 32 | if '30min' in str(file_path): 33 | # 30分钟数据字段映射 34 | df = df.rename(columns={ 35 | '时间': 'date', 36 | '开盘': 'open', 37 | '收盘': 'close', 38 | '最高': 'high', 39 | '最低': 'low', 40 | '成交量': 'vol', 41 | '成交额': 'amount' 42 | }) 43 | else: 44 | # 周线和日线数据保持原有字段名 45 | df['date'] = pd.to_datetime(df['date']) 46 | 47 | logger.debug("开始计算EMA指标") 48 | df['ema_short'] = talib.EMA(df['close'], timeperiod=8) 49 | df['ema_long'] = talib.EMA(df['close'], timeperiod=21) 50 | # 处理NaN和Infinity值 51 | df = df.replace([np.inf, -np.inf], np.nan) 52 | df['ema_short'] = df['ema_short'].fillna(method='ffill').fillna(method='bfill') 53 | df['ema_long'] = df['ema_long'].fillna(method='ffill').fillna(method='bfill') 54 | 55 | logger.debug(f"数据中是否还存在NaN值: {df.isna().any().any()}") 56 | 57 | # 按时间升序排序 58 | df = df.sort_values('date') 59 | logger.info(f"数据时间范围: {df['date'].min()} 至 {df['date'].max()}") 60 | 61 | # 转换为前端需要的格式 62 | columns = ['date', 'open', 'close', 'high', 'low'] 63 | columns.extend(['ema_short', 'ema_long']) 64 | 65 | result = df[columns].to_dict('records') 66 | for item in result: 67 | item['date'] = item['date'].strftime('%Y-%m-%d %H:%M') 68 | # 确保所有数值都是有效的JSON数字 69 | numeric_keys = ['open', 'close', 'high', 'low'] 70 | numeric_keys.extend(['ema_short', 'ema_long']) 71 | 72 | for key in numeric_keys: 73 | if not np.isfinite(item[key]): 74 | item[key] = None 75 | 76 | logger.info(f"数据处理完成，返回 {len(result)} 条记录") 77 | return result 78 | except Exception as e: 79 | error_msg = f"处理数据时发生错误: {str(e)}" 80 | logger.error(error_msg, exc_info=True) 81 | raise HTTPException(status_code=500, detail=error_msg) 82 | 83 | @router.get("/weekly") 84 | async def get_weekly_data(): 85 | logger.info("收到周线数据请求") 86 | data_path = Path("data/159985.SZ_fund_weekly_20190101_20251231.csv") 87 | logger.debug(f"周线数据文件路径: {data_path.absolute()}") 88 | return load_and_process_data(data_path) 89 | 90 | @router.post("/backtest") 91 | async def backtest_strategy(request: BacktestRequest): 92 | """执行策略回测""" 93 | try: 94 | logger.info(f"收到回测请求，use_atr_tp={request.use_atr_tp}, data_period={request.data_period}") 95 | 96 | # 根据选择的周期加载对应的数据文件 97 | if request.data_period == 'weekly': 98 | data_path = Path("data/159985.SZ_fund_weekly_20190101_20251231.csv") 99 | elif request.data_period == 'daily': 100 | data_path = Path("data/159985.SZ_fund_daily_20190101_20251231.csv") 101 | else: # 30min 102 | data_path = Path("data/159985.SZ_fund_30min_20190101_20251231.csv") 103 | 104 | # 读取并处理数据 105 | df = pd.read_csv(data_path) 106 | 107 | # 根据文件名判断数据周期并处理字段名 108 | if '30min' in str(data_path): 109 | # 30分钟数据字段映射 110 | df = df.rename(columns={ 111 | '时间': 'date', 112 | '开盘': 'open', 113 | '收盘': 'close', 114 | '最高': 'high', 115 | '最低': 'low', 116 | '成交量': 'vol', 117 | '成交额': 'amount' 118 | }) 119 | df['date'] = pd.to_datetime(df['date']) 120 | else: 121 | # 转换日期格式 122 | df['date'] = pd.to_datetime(df['date'], format='%Y%m%d') 123 | 124 | # 使用策略类计算信号和执行回测 125 | strategy = DualMAStrategy() 126 | df_with_signals = strategy.calculate_signals(df, use_atr_tp=request.use_atr_tp) 127 | result = strategy.run_backtest(df_with_signals, use_atr_tp=request.use_atr_tp) 128 | 129 | logger.info("回测完成") 130 | return result 131 | 132 | except Exception as e: 133 | error_msg = f"回测过程中发生错误: {str(e)}" 134 | logger.error(error_msg, exc_info=True) 135 | raise HTTPException(status_code=500, detail=error_msg) -------------------------------------------------------------------------------- /backend/routers/fundamental.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException 2 | from services.fundamental import FundamentalAnalyzer 3 | from utils.logger import logger 4 | from datetime import datetime 5 | from typing import Dict, Any 6 | 7 | router = APIRouter() 8 | analyzer = FundamentalAnalyzer() 9 | 10 | @router.get("/analysis") 11 | async def get_fundamental_analysis(date: str): 12 | """获取基本面分析数据""" 13 | try: 14 | # 验证日期格式 15 | try: 16 | datetime.strptime(date, "%Y-%m-%d") 17 | except ValueError: 18 | raise HTTPException(status_code=400, detail="日期格式错误，请使用YYYY-MM-DD格式") 19 | 20 | return await analyzer.get_fundamental_analysis(date) 21 | except Exception as e: 22 | logger.error(f"获取基本面分析失败: {str(e)}") 23 | raise HTTPException(status_code=500, detail=str(e)) 24 | 25 | @router.get("/supply-demand") 26 | async def get_supply_demand() -> Dict[str, Any]: 27 | """获取供需平衡数据""" 28 | try: 29 | return await analyzer.get_supply_demand_data() 30 | except Exception as e: 31 | raise HTTPException(status_code=500, detail=str(e)) 32 | 33 | @router.get("/seasonal") 34 | async def get_seasonal_pattern() -> Dict[str, Any]: 35 | """获取季节性规律数据""" 36 | try: 37 | return {"data": await analyzer.get_seasonal_pattern()} 38 | except Exception as e: 39 | raise HTTPException(status_code=500, detail=str(e)) 40 | 41 | @router.get("/weather") 42 | async def get_weather_data() -> Dict[str, Any]: 43 | """获取天气数据""" 44 | try: 45 | return await analyzer.get_weather_data() 46 | except Exception as e: 47 | raise HTTPException(status_code=500, detail=str(e)) 48 | 49 | @router.get("/crush-profit") 50 | async def get_crush_profit() -> Dict[str, Any]: 51 | """获取压榨利润数据""" 52 | try: 53 | return await analyzer.get_crush_profit() 54 | except Exception as e: 55 | raise HTTPException(status_code=500, detail=str(e)) 56 | 57 | @router.get("/overall") 58 | async def get_overall_assessment() -> Dict[str, Any]: 59 | """获取综合评估数据""" 60 | try: 61 | return await analyzer.get_overall_assessment() 62 | except Exception as e: 63 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /backend/routers/grid.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException 2 | from pathlib import Path 3 | import pandas as pd 4 | import numpy as np 5 | from utils.logger import logger 6 | from datetime import datetime 7 | from typing import List, Dict, Union 8 | from strategies.grid_strategy import GridStrategy 9 | from pydantic import BaseModel, Field 10 | 11 | class BacktestRequest(BaseModel): 12 | grid_levels: int = Field(default=10, ge=2, le=50, description="网格数量，范围2-50") 13 | atr_period: int = Field(default=14, ge=5, le=30, description="ATR周期，范围5-30") 14 | data_period: str = Field(default='daily', description="数据周期: daily/weekly/30min") 15 | 16 | class Config: 17 | schema_extra = { 18 | "example": { 19 | "grid_levels": 10, 20 | "atr_period": 14, 21 | "data_period": "daily" 22 | } 23 | } 24 | 25 | router = APIRouter() 26 | 27 | def load_and_process_data(file_path: Path) -> pd.DataFrame: 28 | """加载并处理数据""" 29 | try: 30 | # 读取CSV文件 31 | df = pd.read_csv(file_path) 32 | logger.info(f"成功读取CSV文件，总数据行数: {len(df)}") 33 | 34 | # 确保必要的列存在 35 | required_columns = ['date', 'open', 'high', 'low', 'close'] 36 | if not all(col in df.columns for col in required_columns): 37 | raise ValueError(f"CSV文件缺少必要的列: {required_columns}") 38 | 39 | # 转换日期格式 40 | df['date'] = pd.to_datetime(df['date'], format='%Y%m%d') 41 | 42 | # 处理NaN和Infinity值 43 | df = df.replace([np.inf, -np.inf], np.nan) 44 | df = df.fillna(method='ffill').fillna(method='bfill') 45 | 46 | # 按日期排序 47 | df = df.sort_values('date') 48 | 49 | # 确保数值列是浮点数类型 50 | numeric_columns = ['open', 'high', 'low', 'close'] 51 | for col in numeric_columns: 52 | df[col] = df[col].astype(float) 53 | 54 | logger.info(f"数据处理完成，时间范围: {df['date'].min()} 至 {df['date'].max()}") 55 | return df 56 | 57 | except Exception as e: 58 | error_msg = f"加载数据文件失败: {str(e)}" 59 | logger.error(error_msg) 60 | raise HTTPException(status_code=500, detail=error_msg) 61 | 62 | @router.post("/backtest") 63 | async def run_backtest(request: BacktestRequest): 64 | try: 65 | logger.info("开始网格策略回测") 66 | logger.info(f"参数: grid_levels={request.grid_levels}, atr_period={request.atr_period}, data_period={request.data_period}") 67 | 68 | # 根据数据周期选择数据文件 69 | data_file = { 70 | 'weekly': 'data/M2501.DCE_future_daily_20240101_20251231.csv', 71 | 'daily': 'data/M2501.DCE_future_daily_20240101_20251231.csv', 72 | '30min': 'data/M2501.DCE_future_daily_20240101_20251231.csv' 73 | }.get(request.data_period) 74 | 75 | if not data_file: 76 | raise HTTPException(status_code=400, detail=f"不支持的数据周期: {request.data_period}") 77 | 78 | # 加载数据 79 | df = load_and_process_data(Path(data_file)) 80 | 81 | # 计算交易信号 82 | df_with_signals = GridStrategy.calculate_signals( 83 | df=df, 84 | grid_levels=request.grid_levels, 85 | atr_period=request.atr_period 86 | ) 87 | 88 | # 执行回测 89 | backtest_results = GridStrategy.run_backtest( 90 | df=df_with_signals, 91 | grid_levels=request.grid_levels 92 | ) 93 | 94 | logger.info("网格策略回测完成") 95 | return { 96 | "status": "success", 97 | "data": { 98 | "backtest_results": backtest_results, 99 | "summary": { 100 | "total_trades": len(backtest_results['trades']), 101 | "win_rate": round(backtest_results['win_rate'], 2), 102 | "total_profit": round(backtest_results['total_profit'], 2), 103 | "sharpe_ratio": round(backtest_results['sharpe_ratio'], 2), 104 | "max_drawdown": round(backtest_results['max_drawdown'], 2), 105 | "annual_returns": round(backtest_results['annual_returns'] * 100, 2) 106 | } 107 | } 108 | } 109 | 110 | except Exception as e: 111 | error_msg = f"执行回测失败: {str(e)}" 112 | logger.error(error_msg) 113 | raise HTTPException(status_code=500, detail=error_msg) 114 | 115 | @router.get("/data") 116 | async def get_grid_data(): 117 | """获取网格策略的最新数据""" 118 | try: 119 | # 使用日线数据 120 | data_file = 'data/M2501.DCE_future_daily_20240101_20251231.csv' 121 | 122 | # 加载数据 123 | df = load_and_process_data(Path(data_file)) 124 | 125 | # 计算网格信号 126 | df_with_signals = GridStrategy.calculate_signals(df) 127 | 128 | # 转换数据格式 129 | result = [] 130 | for _, row in df_with_signals.iterrows(): 131 | # 获取当前网格的价格 132 | grid_price = row['grids'][row['current_grid']] if not pd.isna(row['current_grid']) else None 133 | 134 | # 检查并处理无效的浮点数值 135 | def safe_float(value): 136 | if pd.isna(value) or np.isinf(value): 137 | return None 138 | return float(value) 139 | 140 | result.append({ 141 | 'date': row['date'].strftime('%Y-%m-%d'), 142 | 'open': safe_float(row['open']), 143 | 'close': safe_float(row['close']), 144 | 'high': safe_float(row['high']), 145 | 'low': safe_float(row['low']), 146 | 'grid_level': int(row['current_grid']) if not pd.isna(row['current_grid']) else None, 147 | 'grid_price': safe_float(grid_price) 148 | }) 149 | 150 | return result 151 | 152 | except Exception as e: 153 | error_msg = f"获取网格数据失败: {str(e)}" 154 | logger.error(error_msg) 155 | raise HTTPException(status_code=500, detail=error_msg) -------------------------------------------------------------------------------- /backend/routers/news.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException, Depends, Query 2 | from typing import List, Optional 3 | from services.news_service import NewsService 4 | from utils.logger import logger 5 | from models.news import FlashNews, NewsArticle 6 | 7 | router = APIRouter() 8 | 9 | def get_news_service() -> NewsService: 10 | logger.debug("创建新闻服务实例") 11 | return NewsService() 12 | 13 | @router.get("/daily") 14 | async def get_daily_news( 15 | start_date: Optional[str] = Query(None, description="开始日期，格式：YYYYMMDD"), 16 | end_date: Optional[str] = Query(None, description="结束日期，格式：YYYYMMDD"), 17 | service: NewsService = Depends(get_news_service) 18 | ): 19 | """获取每日新闻""" 20 | try: 21 | news = service.get_news(start_date, end_date) 22 | return news 23 | except Exception as e: 24 | logger.error(f"获取每日新闻失败: {e}") 25 | raise HTTPException(status_code=500, detail=str(e)) 26 | 27 | @router.get("/flash") 28 | async def get_flash_news( 29 | service: NewsService = Depends(get_news_service) 30 | ): 31 | """获取快讯""" 32 | try: 33 | flash_news = service.get_flash_news() 34 | return flash_news 35 | except Exception as e: 36 | logger.error(f"获取快讯失败: {e}") 37 | raise HTTPException(status_code=500, detail=str(e)) 38 | 39 | @router.get("/articles") 40 | async def get_news_articles( 41 | service: NewsService = Depends(get_news_service) 42 | ): 43 | """获取资讯文章""" 44 | try: 45 | articles = service.get_news_articles() 46 | return articles 47 | except Exception as e: 48 | logger.error(f"获取资讯文章失败: {e}") 49 | raise HTTPException(status_code=500, detail=str(e)) 50 | 51 | @router.get("/analysis") 52 | async def get_news_analysis( 53 | news_date: str = Query(..., description="新闻日期，格式：YYYYMMDD"), 54 | service: NewsService = Depends(get_news_service) 55 | ): 56 | """获取新闻分析""" 57 | try: 58 | analysis = service.analyze_news_impact(news_date) 59 | if not analysis: 60 | return { 61 | "date": news_date, 62 | "news_count": 0, 63 | "price_change": None, 64 | "volume_change": None, 65 | "analysis": [], 66 | "message": "未找到该日期的新闻数据" 67 | } 68 | return analysis 69 | except Exception as e: 70 | logger.error(f"获取新闻分析失败: {e}") 71 | raise HTTPException(status_code=500, detail=str(e)) 72 | 73 | @router.post("/analyze") 74 | async def analyze_news( 75 | news_date: str = Query(..., description="新闻日期，格式：YYYYMMDD"), 76 | service: NewsService = Depends(get_news_service) 77 | ): 78 | """分析新闻""" 79 | try: 80 | analysis = await service.analyze_news_with_deepseek(news_date) 81 | return analysis 82 | except Exception as e: 83 | logger.error(f"分析新闻失败: {e}") 84 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /backend/routers/obv_adx_ema.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException 2 | from pathlib import Path 3 | import pandas as pd 4 | import numpy as np 5 | import talib 6 | from utils.logger import logger 7 | from datetime import datetime 8 | from typing import List, Dict, Union 9 | from strategies.obv_adx_ema_strategy import OBVADXEMAStrategy 10 | 11 | router = APIRouter() 12 | 13 | def load_and_process_data(file_path): 14 | try: 15 | logger.info(f"开始加载数据文件: {file_path}") 16 | 17 | if not file_path.exists(): 18 | error_msg = f"数据文件不存在: {file_path}" 19 | logger.error(error_msg) 20 | raise HTTPException(status_code=404, detail=error_msg) 21 | 22 | df = pd.read_csv(file_path) 23 | logger.info(f"成功读取CSV文件，总数据行数: {len(df)}") 24 | 25 | logger.debug("开始处理时间列") 26 | df['date'] = pd.to_datetime(df['date']) 27 | 28 | # 计算技术指标 29 | df['ema20'] = talib.EMA(df['close'], timeperiod=20) 30 | df['ema60'] = talib.EMA(df['close'], timeperiod=60) 31 | df['ema5'] = talib.EMA(df['close'], timeperiod=5) 32 | df['adx'] = talib.ADX(df['high'], df['low'], df['close'], timeperiod=14) 33 | df['obv'] = talib.OBV(df['close'], df['volume']) 34 | df['obv_ma30'] = talib.SMA(df['obv'], timeperiod=30) 35 | 36 | # 处理NaN和Infinity值 37 | df = df.replace([np.inf, -np.inf], np.nan) 38 | df['ema20'] = df['ema20'].fillna(method='ffill').fillna(method='bfill') 39 | df['ema60'] = df['ema60'].fillna(method='ffill').fillna(method='bfill') 40 | df['ema5'] = df['ema5'].fillna(method='ffill').fillna(method='bfill') 41 | df['adx'] = df['adx'].fillna(method='ffill').fillna(method='bfill') 42 | df['obv'] = df['obv'].fillna(method='ffill').fillna(method='bfill') 43 | df['obv_ma30'] = df['obv_ma30'].fillna(method='ffill').fillna(method='bfill') 44 | 45 | logger.debug(f"数据中是否还存在NaN值: {df.isna().any().any()}") 46 | 47 | # 按时间升序排序 48 | df = df.sort_values('date') 49 | logger.info(f"数据时间范围: {df['date'].min()} 至 {df['date'].max()}") 50 | 51 | # 转换为前端需要的格式 52 | columns = ['date', 'open', 'close', 'high', 'low', 'volume', 'ema20', 'ema60', 'ema5', 'adx', 'obv', 'obv_ma30'] 53 | result = df[columns].to_dict('records') 54 | for item in result: 55 | item['date'] = item['date'].strftime('%Y-%m-%d %H:%M') 56 | # 确保所有数值都是有效的JSON数字 57 | numeric_keys = ['open', 'close', 'high', 'low', 'volume', 'ema20', 'ema60', 'ema5', 'adx', 'obv', 'obv_ma30'] 58 | for key in numeric_keys: 59 | if not np.isfinite(item[key]): 60 | item[key] = None 61 | 62 | logger.info(f"数据处理完成，返回 {len(result)} 条记录") 63 | return result 64 | except Exception as e: 65 | error_msg = f"处理数据时发生错误: {str(e)}" 66 | logger.error(error_msg, exc_info=True) 67 | raise HTTPException(status_code=500, detail=error_msg) 68 | 69 | @router.get("/data") 70 | async def get_data(): 71 | logger.info("收到OBV、ADX与EMA组合策略数据请求") 72 | data_path = Path("data/M2501.DCE_future_60min_20240101_20251231.csv") 73 | logger.debug(f"60分钟数据文件路径: {data_path.absolute()}") 74 | return load_and_process_data(data_path) 75 | 76 | @router.post("/backtest") 77 | async def backtest_strategy(): 78 | """执行策略回测""" 79 | try: 80 | logger.info("收到OBV、ADX与EMA组合策略回测请求") 81 | 82 | # 加载数据 83 | data_path = Path("data/M2501.DCE_future_60min_20240101_20251231.csv") 84 | 85 | # 读取并处理数据 86 | df_60min = pd.read_csv(data_path) 87 | df_60min['date'] = pd.to_datetime(df_60min['date']) 88 | 89 | # 使用策略类计算信号和执行回测 90 | strategy = OBVADXEMAStrategy() 91 | df_with_signals = strategy.calculate_signals(df_60min) 92 | result = strategy.run_backtest(df_with_signals) 93 | 94 | logger.info("OBV、ADX与EMA组合策略回测完成") 95 | return result 96 | 97 | except Exception as e: 98 | error_msg = f"回测过程中发生错误: {str(e)}" 99 | logger.error(error_msg, exc_info=True) 100 | raise HTTPException(status_code=500, detail=error_msg) -------------------------------------------------------------------------------- /backend/routers/signals.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException, Depends, Query 2 | from typing import List, Optional 3 | from datetime import datetime, date 4 | from models.signals import Signal, SignalCreate, SignalUpdate 5 | from services.signals import SignalService 6 | from utils.logger import logger 7 | from models.kline import KLineData, SignalRequest 8 | 9 | router = APIRouter() 10 | 11 | def get_signal_service() -> SignalService: 12 | return SignalService() 13 | 14 | @router.post("/signals", response_model=dict) 15 | async def get_signals( 16 | request: SignalRequest, 17 | signal_service: SignalService = Depends(get_signal_service) 18 | ): 19 | """获取交易信号列表""" 20 | try: 21 | # 将字符串日期转换为datetime对象，设置时间为当天的开始和结束 22 | start = datetime.strptime(request.start_date, "%Y-%m-%d").replace(hour=0, minute=0, second=0) 23 | end = datetime.strptime(request.end_date, "%Y-%m-%d").replace(hour=23, minute=59, second=59) 24 | 25 | # 获取信号 26 | signals, total = signal_service.generate_signals( 27 | start, 28 | end, 29 | request.type, 30 | request.page, 31 | request.page_size, 32 | request.klines 33 | ) 34 | 35 | return { 36 | "signals": signals, 37 | "total": total, 38 | "page": request.page, 39 | "page_size": request.page_size, 40 | "total_pages": (total + request.page_size - 1) // request.page_size 41 | } 42 | except Exception as e: 43 | logger.error(f"获取信号失败: {str(e)}", exc_info=True) 44 | raise HTTPException(status_code=500, detail=str(e)) 45 | 46 | @router.post("/signals", response_model=Signal) 47 | async def create_signal( 48 | signal: SignalCreate, 49 | signal_service: SignalService = Depends(get_signal_service) 50 | ): 51 | """创建新信号""" 52 | try: 53 | return signal_service.create_signal(signal) 54 | except Exception as e: 55 | logger.error(f"创建信号失败: {str(e)}", exc_info=True) 56 | raise HTTPException(status_code=500, detail=str(e)) 57 | 58 | @router.put("/signals/{signal_id}", response_model=Signal) 59 | async def update_signal( 60 | signal_id: str, 61 | signal: SignalUpdate, 62 | signal_service: SignalService = Depends(get_signal_service) 63 | ): 64 | """更新信号""" 65 | try: 66 | return signal_service.update_signal(signal_id, signal) 67 | except Exception as e: 68 | logger.error(f"更新信号失败: {str(e)}", exc_info=True) 69 | raise HTTPException(status_code=500, detail=str(e)) 70 | 71 | @router.delete("/signals/{signal_id}") 72 | async def delete_signal( 73 | signal_id: str, 74 | signal_service: SignalService = Depends(get_signal_service) 75 | ): 76 | """删除信号""" 77 | try: 78 | signal_service.delete_signal(signal_id) 79 | return {"message": "信号已删除"} 80 | except Exception as e: 81 | logger.error(f"删除信号失败: {str(e)}", exc_info=True) 82 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /backend/routers/soybean.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, Depends, HTTPException 2 | from typing import List 3 | from datetime import datetime 4 | from services.soybean import SoybeanService 5 | from models.soybean import SoybeanImport 6 | from utils.logger import logger 7 | 8 | router = APIRouter() 9 | 10 | def get_soybean_service() -> SoybeanService: 11 | logger.debug("创建大豆进口数据服务实例") 12 | return SoybeanService() 13 | 14 | @router.get("/import", response_model=SoybeanImport) 15 | async def get_soybean_import_data( 16 | service: SoybeanService = Depends(get_soybean_service) 17 | ): 18 | """获取大豆进口数据""" 19 | try: 20 | data = service.get_soybean_import_data() 21 | if not data: 22 | raise HTTPException(status_code=404, detail="未找到大豆进口数据") 23 | return data 24 | except Exception as e: 25 | logger.error(f"获取大豆进口数据失败: {e}") 26 | import traceback 27 | traceback.print_exc() 28 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /backend/routers/stockfutures.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException, Depends, Query 2 | from typing import List, Optional 3 | from datetime import datetime 4 | from pydantic import BaseModel 5 | from utils.logger import logger 6 | from services.stockfutures import StockFuturesService 7 | 8 | router = APIRouter() 9 | 10 | class StockRecommendation(BaseModel): 11 | """股票推荐结果模型""" 12 | code: str 13 | name: str 14 | level: str # 推荐级别：强烈推荐/推荐 15 | price: float 16 | change_pct: float 17 | reason: str 18 | 19 | class StockPickingResponse(BaseModel): 20 | """选股返回结果模型""" 21 | timestamp: str 22 | recommendations: List[StockRecommendation] 23 | 24 | def get_stock_futures_service(): 25 | """依赖注入：获取StockFuturesService实例""" 26 | return StockFuturesService() 27 | 28 | @router.post("/stock-picking", response_model=StockPickingResponse) 29 | async def pick_stocks( 30 | limit: Optional[int] = Query(10, description="返回的股票数量，默认10只"), 31 | service: StockFuturesService = Depends(get_stock_futures_service) 32 | ): 33 | """智能选股接口""" 34 | try: 35 | # 获取支撑位附近的股票 36 | stocks_near_support = service.find_stocks_near_support(threshold_percent=0.03) 37 | 38 | if not stocks_near_support: 39 | logger.warning("未找到符合条件的股票") 40 | return StockPickingResponse( 41 | timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 42 | recommendations=[] 43 | ) 44 | 45 | # 转换为推荐结果格式 46 | recommendations = [] 47 | for stock in stocks_near_support: # 已经在service层限制了数量 48 | # 计算推荐级别：距离支撑位越近，级别越高 49 | level = "强烈推荐" if stock['hourly_distance_percent'] < 0.02 else "推荐" 50 | 51 | # 构建推荐理由 52 | reason = ( 53 | f"日线距支撑位{stock['distance_percent']*100:.1f}%，" 54 | f"小时线距支撑位{stock['hourly_distance_percent']*100:.1f}%，" 55 | ) 56 | if 'industry' in stock: 57 | reason += f"所属{stock['industry']}行业，" 58 | reason += "多级别支撑共振，建议关注" 59 | 60 | recommendations.append( 61 | StockRecommendation( 62 | code=stock['ts_code'], 63 | name=stock['name'], 64 | level=level, 65 | price=stock['hourly_latest_price'], # 使用小时级别的最新价格 66 | change_pct=stock['pct_chg'], # 保持使用日线的涨跌幅 67 | reason=reason 68 | ) 69 | ) 70 | 71 | return StockPickingResponse( 72 | timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 73 | recommendations=recommendations 74 | ) 75 | except Exception as e: 76 | logger.error(f"选股失败: {str(e)}", exc_info=True) 77 | raise HTTPException(status_code=500, detail=str(e)) 78 | 79 | @router.get("/hs300-stocks") 80 | async def get_hs300_stocks(service: StockFuturesService = Depends(get_stock_futures_service)): 81 | """获取沪深300成分股列表""" 82 | try: 83 | stocks = service.get_hs300_stocks() 84 | if not stocks: 85 | raise HTTPException(status_code=404, detail="未找到沪深300成分股数据") 86 | return stocks 87 | except Exception as e: 88 | logger.error(f"获取沪深300成分股失败: {str(e)}", exc_info=True) 89 | raise HTTPException(status_code=500, detail=str(e)) 90 | 91 | @router.get("/stock-daily/{ts_code}") 92 | async def get_stock_daily( 93 | ts_code: str, 94 | start_date: Optional[str] = None, 95 | end_date: Optional[str] = None, 96 | service: StockFuturesService = Depends(get_stock_futures_service) 97 | ): 98 | """获取股票日线数据""" 99 | try: 100 | df = service.get_stock_daily(ts_code, start_date, end_date) 101 | if df.empty: 102 | raise HTTPException(status_code=404, detail=f"未找到股票{ts_code}的日线数据") 103 | return df.to_dict(orient='records') 104 | except Exception as e: 105 | logger.error(f"获取股票{ts_code}日线数据失败: {str(e)}", exc_info=True) 106 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /backend/routers/trading.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException, Request, Depends 2 | from typing import List 3 | from models.trading import OptionsStrategy, DailyStrategyAnalysis 4 | from services.trading import TradingService 5 | from utils.logger import logger 6 | import httpx 7 | from config import settings 8 | import json 9 | from openai import OpenAI 10 | from fastapi.responses import StreamingResponse 11 | import asyncio 12 | from starlette.background import BackgroundTask 13 | from datetime import datetime 14 | 15 | router = APIRouter() 16 | 17 | # 初始化OpenAI客户端 18 | client = OpenAI( 19 | api_key=settings.DEEPSEEK_API_KEY, 20 | base_url="https://ark.cn-beijing.volces.com/api/v3/bots" 21 | ) 22 | 23 | def get_trading_service() -> TradingService: 24 | return TradingService() 25 | 26 | async def stream_response(response, request: Request, date: str): 27 | reasoning_content = "" 28 | content = "" 29 | 30 | try: 31 | for chunk in response: 32 | # 检查客户端是否断开连接 33 | if await request.is_disconnected(): 34 | logger.info("客户端断开连接") 35 | break 36 | 37 | try: 38 | if hasattr(chunk, "references"): 39 | pass 40 | if not chunk.choices: 41 | continue 42 | if chunk.choices[0].delta.content: 43 | content += chunk.choices[0].delta.content 44 | yield f"data: {json.dumps({'type': 'content', 'content': content})}\n\n" 45 | elif hasattr(chunk.choices[0].delta, "reasoning_content"): 46 | reasoning_content += chunk.choices[0].delta.reasoning_content 47 | yield f"data: {json.dumps({'type': 'reasoning', 'content': reasoning_content})}\n\n" 48 | 49 | except Exception as e: 50 | logger.error(f"处理chunk时出错: {str(e)}") 51 | continue 52 | 53 | # 发送完成标记 54 | yield f"data: {json.dumps({'type': 'done', 'reasoning': reasoning_content, 'content': content})}\n\n" 55 | 56 | # 保存到数据库 57 | try: 58 | trading_service = TradingService() 59 | analysis = DailyStrategyAnalysis( 60 | date=date, # 使用用户选择的日期 61 | content=content, 62 | reasoning_content=reasoning_content 63 | ) 64 | trading_service.save_strategy_analysis(analysis) 65 | logger.info(f"策略分析已保存到数据库 - 日期: {date}") 66 | except Exception as e: 67 | logger.error(f"保存策略分析到数据库失败: {str(e)}") 68 | 69 | except Exception as e: 70 | logger.error(f"流式响应出错: {str(e)}", exc_info=True) 71 | yield f"data: {json.dumps({'type': 'error', 'message': str(e)})}\n\n" 72 | finally: 73 | logger.info("流式响应结束") 74 | 75 | @router.get("/options") 76 | async def get_options_strategies( 77 | date: str, 78 | request: Request, 79 | trading_service: TradingService = Depends(get_trading_service) 80 | ): 81 | """获取期权策略分析""" 82 | try: 83 | # 首先尝试从数据库获取 84 | analysis = trading_service.get_strategy_analysis(date) 85 | if analysis: 86 | logger.info(f"从数据库获取到策略分析 - 日期: {date}") 87 | return { 88 | "content": analysis.content, 89 | "reasoning_content": analysis.reasoning_content 90 | } 91 | 92 | # 如果数据库中没有，则调用Deepseek API 93 | logger.info("数据库中没有找到策略分析，开始调用Deepseek API") 94 | 95 | # 获取交易数据 96 | trading_data = trading_service.get_trading_data() 97 | if not trading_data or 'raw_data' not in trading_data: 98 | raise HTTPException(status_code=500, detail="获取交易数据失败") 99 | 100 | raw_data = trading_data['raw_data'] 101 | prompt = f"""我需要生成一份提示词，其核心目标是：基于多维度数据生成豆粕主力合约（{raw_data['main_contract']}）下一个交易日的交易操作策略。 目前我已经有如下框架，请帮我把【】内的数据说明用最新的互联网资讯补充完成（最好是今天的），并将补完后的这份提示词完整返回给我。 102 | 请直接返回提示词，不需要其他任何额外的文字，特别是引用参考和来源，不要出现任何的引用和来源，还有markdown语法字符也不能出现。 103 | 104 | ``` 目标：基于多维度数据生成豆粕主力合约（{raw_data['main_contract']}）下一个交易日（{raw_data['next_day']}）的量化策略，不要出现任何的引用和来源。 105 | 一、实时价格与技术指标 106 | {trading_data['price_analysis']} 107 | {trading_data['technical_analysis']} 108 | {trading_data['volume_analysis']} 109 | 二、基本面与市场情绪 【请补充供应端数据（进口大豆到港量、油厂开机率、豆粕库存）、需求端数据（饲料企业采购量、替代品价格）】 110 | 三、国际市场联动 111 | 1、【请补充隔夜CBOT美豆走势情况】 112 | 2、【请补充USDA出口销售数据和巴西贴水情况】 113 | 3、人民币汇率：{raw_data['usd_cny']}。 114 | 四、资金与政策风险 115 | 1、【请补充机构行为，包括净空、净多头寸、机构仓位变化情况】。 116 | 2、政策风险 中美关税：美豆进口关税138%，远月成本支撑，但5月前到港压力主导。 117 | 3、【请补充天气炒作情况】 118 | 4、【请补充基差变化】 119 | 5、【请补充突发事件】 ``` 120 | """ 121 | logger.info(f"生成提示词：{prompt}") 122 | try: 123 | response = client.chat.completions.create( 124 | model="bot-20250329163710-8zcqm", 125 | messages=[{"role": "system", "content": "你是DeepSeek，是一个提示词工程专家"}, {"role": "user", "content": prompt}], 126 | stream=False 127 | ) 128 | content = response.choices[0].message.content 129 | logger.info(f"生成提示词：{content}") 130 | 131 | response = client.chat.completions.create( 132 | model="bot-20250329163710-8zcqm", 133 | messages=[{"role": "system", "content": "现在你是一个豆粕期货量化策略专家，请根据我给你的提示词，生成一份豆粕期货交易操作策略。"}, {"role": "user", "content": content}], 134 | stream=True 135 | ) 136 | 137 | return StreamingResponse( 138 | stream_response(response, request, date), 139 | media_type="text/event-stream", 140 | background=BackgroundTask(logger.info, "请求处理完成") 141 | ) 142 | 143 | except Exception as e: 144 | logger.error(f"API调用失败: {str(e)}", exc_info=True) 145 | raise HTTPException(status_code=500, detail=f"API调用失败: {str(e)}") 146 | 147 | except Exception as e: 148 | logger.error(f"获取期权策略分析失败: {str(e)}", exc_info=True) 149 | import traceback 150 | traceback.print_exc() 151 | raise HTTPException(status_code=500, detail=str(e)) 152 | -------------------------------------------------------------------------------- /backend/routers/trading_model.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException 2 | import numpy as np 3 | import pandas as pd 4 | from typing import List, Dict 5 | from datetime import datetime, timedelta 6 | import tensorflow as tf 7 | from sklearn.preprocessing import MinMaxScaler 8 | 9 | router = APIRouter() 10 | 11 | class TradingModel: 12 | def __init__(self): 13 | self.model = self._build_model() 14 | self.scaler = MinMaxScaler() 15 | 16 | def _build_model(self): 17 | model = tf.keras.Sequential([ 18 | tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(60, 5)), 19 | tf.keras.layers.Dropout(0.2), 20 | tf.keras.layers.LSTM(50, return_sequences=False), 21 | tf.keras.layers.Dropout(0.2), 22 | tf.keras.layers.Dense(1) 23 | ]) 24 | model.compile(optimizer='adam', loss='mse') 25 | return model 26 | 27 | def prepare_data(self, data: List[Dict]): 28 | df = pd.DataFrame(data) 29 | features = ['open', 'high', 'low', 'close', 'vol'] 30 | scaled_data = self.scaler.fit_transform(df[features]) 31 | 32 | X, y = [], [] 33 | for i in range(60, len(scaled_data)): 34 | X.append(scaled_data[i-60:i]) 35 | y.append(scaled_data[i, 3]) # 预测收盘价 36 | 37 | return np.array(X), np.array(y) 38 | 39 | @router.get("/predict") 40 | async def get_prediction( 41 | symbol: str = "M", 42 | days: int = 5 43 | ): 44 | """获取模型预测结果""" 45 | try: 46 | # 这里应该从数据库或API获取历史数据 47 | # 示例返回 48 | return { 49 | "next_day_prediction": 3500.0, 50 | "confidence": 0.85, 51 | "trend": "上涨", 52 | "risk_level": "中等", 53 | "suggested_position": "多头" 54 | } 55 | except Exception as e: 56 | raise HTTPException(status_code=500, detail=str(e)) 57 | 58 | @router.get("/model-performance") 59 | async def get_model_performance(): 60 | """获取模型性能指标""" 61 | try: 62 | return { 63 | "accuracy": 0.78, 64 | "sharpe_ratio": 1.5, 65 | "max_drawdown": 0.15, 66 | "win_rate": 0.65, 67 | "profit_factor": 1.8 68 | } 69 | except Exception as e: 70 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /backend/routers/trend_follow.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException 2 | from pathlib import Path 3 | import pandas as pd 4 | import numpy as np 5 | import talib 6 | from utils.logger import logger 7 | from datetime import datetime 8 | from typing import List, Dict, Union 9 | from strategies.trend_follow_strategy import TrendFollowStrategy 10 | 11 | router = APIRouter() 12 | 13 | def load_and_process_data(file_path, is_15min=False): 14 | try: 15 | logger.info(f"开始加载数据文件: {file_path}") 16 | 17 | if not file_path.exists(): 18 | error_msg = f"数据文件不存在: {file_path}" 19 | logger.error(error_msg) 20 | raise HTTPException(status_code=404, detail=error_msg) 21 | 22 | df = pd.read_csv(file_path) 23 | logger.info(f"成功读取CSV文件，总数据行数: {len(df)}") 24 | 25 | logger.debug("开始处理时间列") 26 | df['date'] = pd.to_datetime(df['date']) 27 | 28 | if is_15min: 29 | logger.debug("开始计算15分钟EMA12和EMA26指标") 30 | df['ema12'] = talib.EMA(df['close'], timeperiod=12) 31 | df['ema26'] = talib.EMA(df['close'], timeperiod=26) 32 | # 处理NaN和Infinity值 33 | df = df.replace([np.inf, -np.inf], np.nan) 34 | df['ema12'] = df['ema12'].fillna(method='ffill').fillna(method='bfill') 35 | df['ema26'] = df['ema26'].fillna(method='ffill').fillna(method='bfill') 36 | else: 37 | logger.debug("开始计算60分钟EMA60指标") 38 | df['ema60'] = talib.EMA(df['close'], timeperiod=60) 39 | # 处理NaN和Infinity值 40 | df = df.replace([np.inf, -np.inf], np.nan) 41 | df['ema60'] = df['ema60'].fillna(method='ffill').fillna(method='bfill') 42 | 43 | logger.debug(f"数据中是否还存在NaN值: {df.isna().any().any()}") 44 | 45 | # 按时间升序排序 46 | df = df.sort_values('date') 47 | logger.info(f"数据时间范围: {df['date'].min()} 至 {df['date'].max()}") 48 | 49 | # 转换为前端需要的格式 50 | columns = ['date', 'open', 'close', 'high', 'low'] 51 | if is_15min: 52 | columns.extend(['ema12', 'ema26']) 53 | else: 54 | columns.append('ema60') 55 | 56 | result = df[columns].to_dict('records') 57 | for item in result: 58 | item['date'] = item['date'].strftime('%Y-%m-%d %H:%M') 59 | # 确保所有数值都是有效的JSON数字 60 | numeric_keys = ['open', 'close', 'high', 'low'] 61 | if is_15min: 62 | numeric_keys.extend(['ema12', 'ema26']) 63 | else: 64 | numeric_keys.append('ema60') 65 | 66 | for key in numeric_keys: 67 | if not np.isfinite(item[key]): 68 | item[key] = None 69 | 70 | logger.info(f"数据处理完成，返回 {len(result)} 条记录") 71 | return result 72 | except Exception as e: 73 | error_msg = f"处理数据时发生错误: {str(e)}" 74 | logger.error(error_msg, exc_info=True) 75 | raise HTTPException(status_code=500, detail=error_msg) 76 | 77 | @router.get("/15min") 78 | async def get_15min_data(): 79 | logger.info("收到15分钟数据请求") 80 | data_path = Path("data/M2501.DCE_future_15min_20240101_20251231.csv") 81 | logger.debug(f"15分钟数据文件路径: {data_path.absolute()}") 82 | return load_and_process_data(data_path, is_15min=True) 83 | 84 | @router.get("/60min") 85 | async def get_60min_data(): 86 | logger.info("收到60分钟数据请求") 87 | data_path = Path("data/M2501.DCE_future_60min_20240101_20251231.csv") 88 | logger.debug(f"60分钟数据文件路径: {data_path.absolute()}") 89 | return load_and_process_data(data_path, is_15min=False) 90 | 91 | @router.post("/backtest") 92 | async def backtest_strategy(): 93 | """执行策略回测""" 94 | try: 95 | logger.info("收到回测请求") 96 | 97 | # 加载数据 98 | data_path_15min = Path("data/M2501.DCE_future_15min_20240101_20251231.csv") 99 | data_path_60min = Path("data/M2501.DCE_future_60min_20240101_20251231.csv") 100 | 101 | # 读取并处理数据 102 | df_15min = pd.read_csv(data_path_15min) 103 | df_60min = pd.read_csv(data_path_60min) 104 | 105 | df_15min['date'] = pd.to_datetime(df_15min['date']) 106 | df_60min['date'] = pd.to_datetime(df_60min['date']) 107 | 108 | # 计算技术指标 109 | df_15min['ema12'] = talib.EMA(df_15min['close'], timeperiod=12) 110 | df_15min['ema26'] = talib.EMA(df_15min['close'], timeperiod=26) 111 | df_60min['ema60'] = talib.EMA(df_60min['close'], timeperiod=60) 112 | 113 | # 使用策略类计算信号和执行回测 114 | strategy = TrendFollowStrategy() 115 | df_with_signals = strategy.calculate_signals(df_15min, df_60min) 116 | result = strategy.run_backtest(df_with_signals) 117 | 118 | logger.info("回测完成") 119 | return result 120 | 121 | except Exception as e: 122 | error_msg = f"回测过程中发生错误: {str(e)}" 123 | logger.error(error_msg, exc_info=True) 124 | raise HTTPException(status_code=500, detail=error_msg) -------------------------------------------------------------------------------- /backend/services/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Business Services 3 | """ -------------------------------------------------------------------------------- /backend/services/account.py: -------------------------------------------------------------------------------- 1 | from sqlalchemy import create_engine 2 | from sqlalchemy.orm import sessionmaker 3 | from models.account import AccountDB 4 | from config import settings 5 | from utils.logger import logger 6 | import uuid 7 | 8 | class AccountService: 9 | def __init__(self): 10 | self.engine = create_engine(settings.DATABASE_URL or "sqlite:///./trading.db") 11 | AccountDB.metadata.create_all(self.engine) 12 | self.SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=self.engine) 13 | self.logger = logger 14 | 15 | # 初始化账户 16 | self._init_account() 17 | 18 | def _init_account(self) -> None: 19 | """初始化账户""" 20 | try: 21 | db = self.SessionLocal() 22 | # 检查是否已存在账户 23 | account = db.query(AccountDB).first() 24 | if not account: 25 | # 创建新账户 26 | account = AccountDB( 27 | initial_balance=1000000.0, # 初始资金100万 28 | current_balance=1000000.0, # 当前资产等于初始资金 29 | available_balance=1000000.0, # 可用资金等于当前资产 30 | total_profit=0.0, 31 | total_commission=0.0, 32 | position_cost=0.0, 33 | position_quantity=0 34 | ) 35 | db.add(account) 36 | db.commit() 37 | self.logger.info("账户初始化成功") 38 | except Exception as e: 39 | self.logger.error(f"初始化账户失败: {e}") 40 | db.rollback() 41 | raise 42 | 43 | def get_account(self) -> AccountDB: 44 | """获取账户信息""" 45 | try: 46 | db = self.SessionLocal() 47 | account = db.query(AccountDB).first() 48 | return account 49 | except Exception as e: 50 | self.logger.error(f"获取账户信息失败: {e}") 51 | raise 52 | finally: 53 | db.close() 54 | 55 | def update_account(self, account: AccountDB) -> None: 56 | """更新账户信息""" 57 | try: 58 | db = self.SessionLocal() 59 | db_account = db.query(AccountDB).first() 60 | if not db_account: 61 | raise ValueError("账户不存在") 62 | 63 | # 更新账户信息 64 | db_account.current_balance = account.current_balance 65 | db_account.available_balance = account.available_balance 66 | db_account.total_profit = account.total_profit 67 | db_account.total_commission = account.total_commission 68 | db_account.position_cost = account.position_cost 69 | db_account.position_quantity = account.position_quantity 70 | 71 | db.commit() 72 | self.logger.info("账户信息更新成功") 73 | except Exception as e: 74 | self.logger.error(f"更新账户信息失败: {e}") 75 | db.rollback() 76 | raise 77 | finally: 78 | db.close() 79 | 80 | def update_balance(self, profit: float, commission: float) -> None: 81 | """更新账户余额""" 82 | try: 83 | db = self.SessionLocal() 84 | account = db.query(AccountDB).first() 85 | if not account: 86 | raise ValueError("账户不存在") 87 | 88 | # 更新账户余额 89 | account.current_balance += profit 90 | account.available_balance += profit 91 | account.total_profit += profit 92 | account.total_commission += commission 93 | 94 | db.commit() 95 | self.logger.info(f"账户余额更新成功: 盈亏={profit}, 手续费={commission}") 96 | except Exception as e: 97 | self.logger.error(f"更新账户余额失败: {e}") 98 | db.rollback() 99 | raise 100 | finally: 101 | db.close() -------------------------------------------------------------------------------- /backend/services/position.py: -------------------------------------------------------------------------------- 1 | from sqlalchemy import create_engine 2 | from sqlalchemy.orm import Session, sessionmaker 3 | from models.position import PositionDB, Position, PositionCreate 4 | from utils.logger import logger 5 | from typing import List, Optional 6 | from config import settings 7 | 8 | class PositionService: 9 | def __init__(self, db: Session = None): 10 | self.engine = create_engine(settings.DATABASE_URL or "sqlite:///./trading.db") 11 | PositionDB.metadata.create_all(self.engine) 12 | self.SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=self.engine) 13 | self.logger = logger 14 | 15 | def get_position(self, symbol: str) -> Optional[PositionDB]: 16 | """获取指定品种的持仓""" 17 | try: 18 | db = self.SessionLocal() 19 | self.logger.info(f"尝试获取持仓，品种: {symbol}") 20 | position = db.query(PositionDB).filter( 21 | PositionDB.symbol == symbol, 22 | PositionDB.status.in_(['open', 'partial_closed']) # 同时查询open和partial_closed状态的持仓 23 | ).first() 24 | self.logger.info(f"查询结果: {position}") 25 | if position: 26 | self.logger.info(f"持仓详情 - ID: {position.id}, 品种: {position.symbol}, 状态: {position.status}, 数量: {position.quantity}, 价格: {position.price}") 27 | return position 28 | except Exception as e: 29 | self.logger.error(f"获取持仓失败: {e}") 30 | return None 31 | finally: 32 | db.close() 33 | 34 | def create_position(self, position: PositionCreate) -> PositionDB: 35 | """创建新持仓""" 36 | try: 37 | db = self.SessionLocal() 38 | db_position = PositionDB(**position.dict()) 39 | db.add(db_position) 40 | db.commit() 41 | db.refresh(db_position) 42 | return db_position 43 | except Exception as e: 44 | self.logger.error(f"创建持仓失败: {e}") 45 | db.rollback() 46 | raise 47 | finally: 48 | db.close() 49 | 50 | def update_position(self, position: Position) -> Position: 51 | """更新持仓信息""" 52 | try: 53 | db = self.SessionLocal() 54 | db_position = db.query(PositionDB).filter(PositionDB.id == position.id).first() 55 | if db_position: 56 | db_position.symbol = position.symbol 57 | db_position.price = position.price 58 | db_position.quantity = position.quantity 59 | db_position.status = position.status 60 | db.commit() 61 | db.refresh(db_position) 62 | return Position( 63 | id=db_position.id, 64 | symbol=db_position.symbol, 65 | price=db_position.price, 66 | quantity=db_position.quantity, 67 | status=db_position.status, 68 | created_at=db_position.created_at, 69 | updated_at=db_position.updated_at 70 | ) 71 | else: 72 | raise ValueError(f"Position with id {position.id} not found") 73 | except Exception as e: 74 | self.logger.error(f"更新持仓失败: {e}") 75 | db.rollback() 76 | raise 77 | finally: 78 | db.close() 79 | 80 | def close_position(self, symbol: str) -> None: 81 | """平仓""" 82 | try: 83 | db = self.SessionLocal() 84 | position = self.get_position(symbol) 85 | if position: 86 | position.status = 'closed' 87 | db.commit() 88 | except Exception as e: 89 | self.logger.error(f"平仓失败: {e}") 90 | db.rollback() 91 | raise 92 | finally: 93 | db.close() 94 | 95 | def delete_position(self, position_id: str) -> None: 96 | """删除持仓""" 97 | try: 98 | db = self.SessionLocal() 99 | position = db.query(PositionDB).filter(PositionDB.id == position_id).first() 100 | if not position: 101 | raise ValueError(f"持仓不存在: {position_id}") 102 | 103 | db.delete(position) 104 | db.commit() 105 | self.logger.info(f"删除持仓成功: {position_id}") 106 | except Exception as e: 107 | self.logger.error(f"删除持仓失败: {e}") 108 | db.rollback() 109 | raise 110 | finally: 111 | db.close() -------------------------------------------------------------------------------- /backend/test/test.py: -------------------------------------------------------------------------------- 1 | import akshare as ak 2 | 3 | futures_zh_spot_df = ak.futures_zh_spot(symbol='M2601', market="CF", adjust='0') 4 | print(futures_zh_spot_df) 5 | 6 | futures_zh_realtime_df = ak.futures_zh_realtime(symbol="豆粕") 7 | print(futures_zh_realtime_df) -------------------------------------------------------------------------------- /backend/tools/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Tools for fetching data from Tushare 3 | """ -------------------------------------------------------------------------------- /backend/trading.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sencloud/m_quant/e5a957986954fc95ffaa2a815ccdd1744001fac3/backend/trading.db -------------------------------------------------------------------------------- /backend/utils/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Utility Functions 3 | """ -------------------------------------------------------------------------------- /backend/utils/logger.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from loguru import logger 3 | from pathlib import Path 4 | 5 | # 创建日志目录 6 | log_path = Path("logs") 7 | log_path.mkdir(exist_ok=True) 8 | 9 | # 配置日志 10 | logger.remove() # 移除默认的处理器 11 | 12 | # 添加控制台处理器 13 | logger.add( 14 | sys.stderr, 15 | format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}", 16 | level="INFO" 17 | ) 18 | 19 | # 添加文件处理器 20 | logger.add( 21 | "logs/app_{time}.log", 22 | rotation="500 MB", 23 | retention="10 days", 24 | format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}", 25 | level="DEBUG", 26 | encoding="utf-8" 27 | ) -------------------------------------------------------------------------------- /docs/多品种套利.md: -------------------------------------------------------------------------------- 1 | 针对豆二、豆粕、豆油三者的日内动态套利设计，需结合三者价格关联性、市场流动性及日内波动规律，以下提供可实现日均200元盈利的低风险策略框架及操作要点： 2 | 3 | --- 4 | 5 | ### 一、策略核心逻辑 6 | 1. **套利基础** 7 | 三者存在压榨关系：100%大豆≈18%豆油+80%豆粕+1.5%损耗。这一比例关系决定了三者价格存在长期均衡性，但日内可能因供需错配或资金流动出现短期偏离。 8 | • **油粕比套利**：豆油与豆粕价格比值（油粕比）通常波动于1.8-2.8区间，日内可捕捉比值回归的波动。 9 | • **压榨利润套利**：当压榨利润（豆油+豆粕价格-大豆成本）偏离正常水平时，可通过多空组合对冲套利。 10 | 11 | 2. **日内动态性** 12 | 重点关注以下日内波动规律： 13 | • **早盘高波动**（9:00-10:30）：市场消化隔夜外盘（如CBOT大豆）及现货数据，油粕比易出现跳空缺口。 14 | • **午盘震荡**（10:30-14:30）：资金博弈加剧，压榨利润套利机会增多。 15 | • **尾盘修正**（14:30-15:00）：部分套利资金平仓，价差回归概率较高。 16 | 17 | --- 18 | 19 | ### 二、具体策略设计 20 | #### 策略1：油粕比日内波段套利 21 | 1. **触发条件** 22 | • **比值突破阈值**：当实时油粕比（豆油价格/豆粕价格）高于2.5或低于2.0时，视为短期超买/超卖信号。 23 | • **量价配合**：结合成交量放大（如主力合约成交量较前5分钟均值增长30%以上）确认趋势有效性。 24 | 25 | 2. **操作方向** 26 | • **比值高位（>2.5）**：做空油粕比（卖出豆油、买入豆粕）。 27 | • **比值低位（<2.0）**：做多油粕比（买入豆油、卖出豆粕）。 28 | 29 | 3. **止盈止损** 30 | • **目标盈利**：单次交易目标价差回归0.2-0.3点（对应约200-300元/组）。 31 | • **止损设置**：价差反向扩大0.1点时止损，单笔最大亏损控制在100元以内。 32 | 33 | **示例**：若豆油现价8000元/吨，豆粕现价3500元/吨，油粕比=2.29。当比值快速升至2.5时，卖出1手豆油（10吨）并买入2.5手豆粕（豆粕合约单位10吨，需按比例匹配），待比值回落至2.3时平仓，盈利约（2.5-2.3）×3500×2.5手=1750元。分摊至日内多次交易，可实现目标收益。 34 | 35 | --- 36 | 37 | #### 策略2：压榨利润均值回归套利 38 | 1. **触发条件** 39 | • **压榨利润偏离**：实时压榨利润=（豆油价格×0.18 +豆粕价格×0.8） - 豆二价格。若利润较前5日均值偏离50元/吨以上，视为套利机会。 40 | 41 | 2. **操作方向** 42 | • **利润过高**：做空压榨利润（卖出豆油+豆粕，买入豆二）。 43 | • **利润过低**：做多压榨利润（买入豆油+豆粕，卖出豆二）。 44 | 45 | 3. **风控要点** 46 | • **合约选择**：优先选择主力合约（如豆二2409、豆粕2409、豆油2409）以保证流动性。 47 | • **对冲比例**：按压榨比例1:0.18:0.8（豆二:豆油:豆粕）动态调整持仓权重。 48 | 49 | **示例**：若豆二价格4000元/吨，豆油8000元/吨，豆粕3500元/吨，压榨利润=（8000×0.18+3500×0.8）-4000=1440+2800-4000=240元/吨。假设历史均值为150元/吨，则做空压榨利润（卖出豆油+豆粕，买入豆二），待利润回归至200元时平仓，盈利约40元/吨×10吨（1手）=400元。 50 | 51 | --- 52 | 53 | ### 三、执行优化建议 54 | 1. **工具辅助** 55 | • 使用量化软件（如文华财经、TB）监控实时价差及压榨利润，设置自动报警阈值。 56 | • 参考主力资金流向（如网页9中的资金净流入数据）判断短期趋势延续性。 57 | 58 | 2. **成本控制** 59 | • **手续费**：选择低佣金账户，单笔交易总成本（开平仓）控制在20元以内。 60 | • **滑点管理**：避开开盘前10分钟及重大数据发布时段（如USDA报告），减少滑点损失。 61 | 62 | 3. **风险规避** 63 | • **单一策略日内交易不超过3次**，避免过度交易导致摩擦成本累积。 64 | • **极端行情暂停**：若日内波动率超过2%，或主力合约持仓量骤降20%以上，暂停套利。 65 | 66 | -------------------------------------------------------------------------------- /frontend/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "soybean-trading-frontend", 3 | "version": "0.1.0", 4 | "private": true, 5 | "dependencies": { 6 | "@ant-design/plots": "^2.3.3", 7 | "@headlessui/react": "^1.7.17", 8 | "@heroicons/react": "^2.0.18", 9 | "@tanstack/react-query": "^4.36.1", 10 | "@types/antd": "^1.0.4", 11 | "@types/react-syntax-highlighter": "^15.5.13", 12 | "@uiw/react-md-editor": "^4.0.5", 13 | "antd": "^5.24.5", 14 | "axios": "^1.6.2", 15 | "chart.js": "^4.4.1", 16 | "echarts": "^5.6.0", 17 | "echarts-for-react": "^3.0.2", 18 | "prism-react-renderer": "^2.4.1", 19 | "react": "^18.2.0", 20 | "react-chartjs-2": "^5.2.0", 21 | "react-dom": "^18.2.0", 22 | "react-markdown": "^10.1.0", 23 | "react-router-dom": "^6.20.1", 24 | "react-scripts": "5.0.1", 25 | "react-syntax-highlighter": "^15.6.1", 26 | "remark-gfm": "^4.0.1", 27 | "typescript": "^4.9.5", 28 | "web-vitals": "^2.1.4" 29 | }, 30 | "scripts": { 31 | "start": "set DISABLE_STRICT_MODE=true && react-scripts start", 32 | "build": "react-scripts build", 33 | "test": "react-scripts test", 34 | "eject": "react-scripts eject" 35 | }, 36 | "eslintConfig": { 37 | "extends": [ 38 | "react-app", 39 | "react-app/jest" 40 | ] 41 | }, 42 | "browserslist": { 43 | "production": [ 44 | ">0.2%", 45 | "not dead", 46 | "not op_mini all" 47 | ], 48 | "development": [ 49 | "last 1 chrome version", 50 | "last 1 firefox version", 51 | "last 1 safari version" 52 | ] 53 | }, 54 | "devDependencies": { 55 | "@types/chart.js": "^2.9.41", 56 | "@types/node": "^22.13.14", 57 | "@types/react": "^19.0.12", 58 | "@types/react-dom": "^19.0.4", 59 | "@types/react-router-dom": "^5.3.3", 60 | "autoprefixer": "^10.4.16", 61 | "postcss": "^8.4.31", 62 | "tailwindcss": "^3.3.5" 63 | } 64 | } 65 | -------------------------------------------------------------------------------- /frontend/postcss.config.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | plugins: { 3 | tailwindcss: {}, 4 | autoprefixer: {}, 5 | }, 6 | } -------------------------------------------------------------------------------- /frontend/public/donate.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sencloud/m_quant/e5a957986954fc95ffaa2a815ccdd1744001fac3/frontend/public/donate.jpg -------------------------------------------------------------------------------- /frontend/public/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sencloud/m_quant/e5a957986954fc95ffaa2a815ccdd1744001fac3/frontend/public/favicon.ico -------------------------------------------------------------------------------- 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16 | 17 | -------------------------------------------------------------------------------- /frontend/public/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sencloud/m_quant/e5a957986954fc95ffaa2a815ccdd1744001fac3/frontend/public/logo.png -------------------------------------------------------------------------------- /frontend/public/logo192.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sencloud/m_quant/e5a957986954fc95ffaa2a815ccdd1744001fac3/frontend/public/logo192.png -------------------------------------------------------------------------------- /frontend/public/manifest.json: -------------------------------------------------------------------------------- 1 | { 2 | "short_name": "豆粕交易", 3 | "name": "新致量化策略", 4 | "icons": [ 5 | { 6 | "src": "favicon.ico", 7 | "sizes": "64x64 32x32 24x24 16x16", 8 | "type": "image/x-icon" 9 | }, 10 | { 11 | "src": "logo192.png", 12 | "type": "image/png", 13 | "sizes": "192x192" 14 | }, 15 | { 16 | "src": "logo512.png", 17 | "type": "image/png", 18 | "sizes": "512x512" 19 | } 20 | ], 21 | "start_url": ".", 22 | "display": "standalone", 23 | "theme_color": "#000000", 24 | "background_color": "#ffffff" 25 | } -------------------------------------------------------------------------------- /frontend/src/App.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import { BrowserRouter as Router, Routes, Route } from 'react-router-dom'; 3 | import { QueryClient, QueryClientProvider } from '@tanstack/react-query'; 4 | import Home from './pages/Home'; 5 | import Trading from './pages/Trading'; 6 | import Research from './pages/Research'; 7 | import StrategyAdvicePage from './pages/StrategyAdvicePage'; 8 | import CoreFactorAnalysisPage from './pages/CoreFactorAnalysisPage'; 9 | import OptionsStrategyPage from './pages/OptionsStrategyPage'; 10 | import ProAnalysis from './pages/ProAnalysis'; 11 | import NewsAnalysis from './pages/NewsAnalysis'; 12 | import Agents from './pages/Agents'; 13 | import PrivacyPolicy from './pages/PrivacyPolicy'; 14 | import TermsOfService from './pages/TermsOfService'; 15 | import Disclaimer from './pages/Disclaimer'; 16 | import Signals from './pages/Signals'; 17 | import MultiVarietyArbitrage from './pages/MultiVarietyArbitrage'; 18 | import StockFutures from './pages/pro/StockFutures'; 19 | import SoybeanImport from './pages/pro/SoybeanImport'; 20 | import MarketView from './pages/MarketView'; 21 | import HoldingAnalysis from './pages/HoldingAnalysis'; 22 | 23 | const queryClient = new QueryClient(); 24 | 25 | const App: React.FC = () => { 26 | return ( 27 | 28 | 29 | 30 | } /> 31 | } /> 32 | } /> 33 | } /> 34 | } /> 35 | } /> 36 | } /> 37 | } /> 38 | } /> 39 | } /> 40 | } /> 41 | } /> 42 | } /> 43 | } /> 44 | } /> 45 | } /> 46 | } /> 47 | } /> 48 | {/* 其他路由将在后续添加 */} 49 | 50 | 51 | 52 | ); 53 | }; 54 | 55 | export default App; -------------------------------------------------------------------------------- /frontend/src/api/ai.ts: -------------------------------------------------------------------------------- 1 | import { API_BASE_URL } from '../config/api'; 2 | import axios from 'axios'; 3 | 4 | export interface BacktestRequest { 5 | tickers: string[]; 6 | start_date: string; 7 | end_date: string; 8 | initial_capital?: number; 9 | portfolio: { [key: string]: number }; 10 | selected_analysts?: string[]; 11 | model_name?: string; 12 | model_provider?: string; 13 | } 14 | 15 | export interface BacktestResponse { 16 | analysis: { 17 | decisions: any; 18 | analyst_signals: any; 19 | }; 20 | backtest: { 21 | portfolio_values: number[]; 22 | performance_metrics: { 23 | total_return: number; 24 | annualized_return: number; 25 | sharpe_ratio: number; 26 | max_drawdown: number; 27 | volatility: number; 28 | }; 29 | }; 30 | } 31 | 32 | export const runBacktest = async (request: BacktestRequest): Promise => { 33 | try { 34 | const response = await axios.post(`${API_BASE_URL}/ai/backtest`, request); 35 | return response.data; 36 | } catch (error) { 37 | console.error('Error running backtest:', error); 38 | throw error; 39 | } 40 | }; -------------------------------------------------------------------------------- /frontend/src/api/arbitrage.ts: -------------------------------------------------------------------------------- 1 | import axios from 'axios'; 2 | import { API_BASE_URL } from '../config/api'; 3 | 4 | export interface SpreadData { 5 | date: string; 6 | spread: number; 7 | } 8 | 9 | export const getSpreadData = async ( 10 | startDate?: string, 11 | endDate?: string, 12 | nearContract: string = 'M2509', 13 | farContract: string = 'M2601' 14 | ): Promise => { 15 | const params = new URLSearchParams(); 16 | if (startDate) params.append('start_date', startDate); 17 | if (endDate) params.append('end_date', endDate); 18 | params.append('near_contract', nearContract); 19 | params.append('far_contract', farContract); 20 | 21 | const response = await axios.get(`${API_BASE_URL}/arbitrage/spread?${params.toString()}`); 22 | return response.data; 23 | }; 24 | 25 | export const getInterSpeciesSpreadData = async ( 26 | start_date?: string, 27 | end_date?: string, 28 | contract1?: string, 29 | contract2?: string 30 | ): Promise => { 31 | try { 32 | const response = await axios.get(`${API_BASE_URL}/arbitrage/inter_species_spread`, { 33 | params: { 34 | start_date, 35 | end_date, 36 | contract1, 37 | contract2 38 | } 39 | }); 40 | return response.data; 41 | } catch (error) { 42 | console.error('获取跨品种价差数据失败:', error); 43 | return []; 44 | } 45 | }; -------------------------------------------------------------------------------- /frontend/src/api/signals.ts: -------------------------------------------------------------------------------- 1 | import { API_BASE_URL } from '../config/api'; 2 | import axios from 'axios'; 3 | 4 | export type SignalStatus = 'open' | 'closed' | 'partial_closed'; 5 | 6 | export interface Signal { 7 | id: string; 8 | date: string; 9 | symbol: string; 10 | symbol_type: string; 11 | type: 'BUY_OPEN' | 'SELL_OPEN' | 'BUY_CLOSE' | 'SELL_CLOSE'; 12 | price: number; 13 | quantity: number; 14 | status: SignalStatus; 15 | reason: string; 16 | close_date?: string; 17 | close_price?: number; 18 | profit: number; 19 | created_at: string; 20 | updated_at: string; 21 | } 22 | 23 | export interface SignalCreate { 24 | date: string; 25 | symbol: string; 26 | symbol_type: string; 27 | type: 'BUY_OPEN' | 'SELL_OPEN' | 'BUY_CLOSE' | 'SELL_CLOSE'; 28 | price: number; 29 | quantity: number; 30 | status: 'open' | 'closed'; 31 | reason: string; 32 | close_date?: string; 33 | close_price?: number; 34 | profit: number; 35 | } 36 | 37 | export interface SignalUpdate { 38 | status?: 'open' | 'closed'; 39 | close_date?: string; 40 | close_price?: number; 41 | profit?: number; 42 | } 43 | 44 | export interface SignalResponse { 45 | signals: Signal[]; 46 | } 47 | 48 | export const getSignals = async ( 49 | startDate: string, 50 | endDate: string, 51 | type?: string 52 | ): Promise => { 53 | try { 54 | const response = await axios.get(`${API_BASE_URL}/signals`, { 55 | params: { 56 | start_date: startDate, 57 | end_date: endDate, 58 | type 59 | } 60 | }); 61 | return response.data; 62 | } catch (error) { 63 | console.error('获取信号失败:', error); 64 | throw error; 65 | } 66 | }; 67 | 68 | export const createSignal = async (signal: SignalCreate): Promise => { 69 | try { 70 | const response = await axios.post(`${API_BASE_URL}/signals`, signal); 71 | return response.data; 72 | } catch (error) { 73 | console.error('创建信号失败:', error); 74 | throw error; 75 | } 76 | }; 77 | 78 | export const updateSignal = async (signalId: string, signal: SignalUpdate): Promise => { 79 | try { 80 | const response = await axios.put(`${API_BASE_URL}/signals/${signalId}`, signal); 81 | return response.data; 82 | } catch (error) { 83 | console.error('更新信号失败:', error); 84 | throw error; 85 | } 86 | }; 87 | 88 | export const deleteSignal = async (signalId: string): Promise => { 89 | try { 90 | await axios.delete(`${API_BASE_URL}/signals/${signalId}`); 91 | } catch (error) { 92 | console.error('删除信号失败:', error); 93 | throw error; 94 | } 95 | }; -------------------------------------------------------------------------------- /frontend/src/api/soybean.ts: -------------------------------------------------------------------------------- 1 | import axios from 'axios'; 2 | import { API_BASE_URL } from '../config/api'; 3 | 4 | export interface ComparisonData { 5 | month: string; 6 | value: number; 7 | type: string; 8 | } 9 | 10 | export interface PortDistributionData { 11 | port: string; 12 | value: number; 13 | type: string; 14 | } 15 | 16 | export interface PortData { 17 | port: string; 18 | current: number; 19 | next_month: number; 20 | next_two_month: number; 21 | } 22 | 23 | export interface CustomsData { 24 | customs: string; 25 | current: number; 26 | next_period: number; 27 | next_month: number; 28 | next_two_month: number; 29 | } 30 | 31 | export interface SoybeanImportData { 32 | current_shipment: number; 33 | current_shipment_yoy: number; 34 | forecast_shipment: number; 35 | forecast_shipment_yoy: number; 36 | current_arrival: number; 37 | current_arrival_yoy: number; 38 | next_arrival: number; 39 | next_arrival_yoy: number; 40 | monthly_comparison: ComparisonData[]; 41 | port_distribution: PortDistributionData[]; 42 | port_details: PortData[]; 43 | customs_details: CustomsData[]; 44 | } 45 | 46 | export const fetchSoybeanImportData = async (): Promise => { 47 | const response = await axios.get(`${API_BASE_URL}/soybean/import`); 48 | return response.data; 49 | }; -------------------------------------------------------------------------------- /frontend/src/components/SRLevels.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | 3 | interface SRLevel { 4 | price: number; 5 | type: 'Support' | 'Resistance'; 6 | strength: number; 7 | start_time: string; 8 | break_time: string | null; 9 | retest_times: string[]; 10 | timeframe: string; 11 | } 12 | 13 | interface SRLevelsProps { 14 | levels: SRLevel[]; 15 | } 16 | 17 | const SRLevels: React.FC = ({ levels }) => { 18 | // 只显示最近90天的数据，并且只显示未突破的支撑阻力位 19 | const ninetyDaysAgo = new Date(); 20 | ninetyDaysAgo.setDate(ninetyDaysAgo.getDate() - 90); 21 | 22 | const recentLevels = levels.filter(level => { 23 | const startTime = new Date(level.start_time); 24 | return startTime >= ninetyDaysAgo && !level.break_time; // 只显示未突破的 25 | }); 26 | 27 | // 按强度排序 28 | const sortedLevels = recentLevels.sort((a, b) => b.strength - a.strength); 29 | 30 | const supportLevels = sortedLevels.filter(level => level.type === 'Support'); 31 | const resistanceLevels = sortedLevels.filter(level => level.type === 'Resistance'); 32 | 33 | const renderLevel = (level: SRLevel) => { 34 | const isRetesting = level.retest_times.length > 0; 35 | 36 | return ( 37 |
39 |
40 |
41 |
44 | 47 | {level.price.toFixed(2)} 48 | 49 |
50 |
51 | 52 | 有效 53 | 54 | {isRetesting && ( 55 | 56 | 回测中 57 | 58 | )} 59 |
60 | {[...Array(level.strength)].map((_, i) => ( 61 |
62 | ))} 63 |
64 |
65 |
66 |
67 |
开始: {new Date(level.start_time).toLocaleDateString()}
68 | {isRetesting && ( 69 |
70 | 最近回测: {new Date(level.retest_times[level.retest_times.length - 1]).toLocaleDateString()} 71 |
72 | )} 73 |
74 |
75 | ); 76 | }; 77 | 78 | return ( 79 |
80 |
81 |

支撑位

82 |
83 | {supportLevels.map(renderLevel)} 84 |
85 |
86 | 87 |
88 |

阻力位

89 |
90 | {resistanceLevels.map(renderLevel)} 91 |
92 |
93 |
94 | ); 95 | }; 96 | 97 | export default SRLevels; -------------------------------------------------------------------------------- /frontend/src/components/Signallet.css: -------------------------------------------------------------------------------- 1 | .signallet { 2 | background: white; 3 | border-radius: 8px; 4 | box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); 5 | width: 300px; 6 | padding: 16px; 7 | margin: 16px; 8 | } 9 | 10 | .signallet-header { 11 | margin-bottom: 16px; 12 | } 13 | 14 | .signallet-header h2 { 15 | font-size: 18px; 16 | font-weight: 600; 17 | color: #1a1a1a; 18 | margin: 0; 19 | } 20 | 21 | .signallet-stats { 22 | display: grid; 23 | grid-template-columns: repeat(3, 1fr); 24 | gap: 8px; 25 | margin-bottom: 16px; 26 | padding: 8px; 27 | background: #f5f5f5; 28 | border-radius: 6px; 29 | } 30 | 31 | .stat-item { 32 | display: flex; 33 | flex-direction: column; 34 | align-items: center; 35 | text-align: center; 36 | } 37 | 38 | .stat-item span:first-child { 39 | font-size: 12px; 40 | color: #666; 41 | } 42 | 43 | .stat-item .value { 44 | font-size: 14px; 45 | font-weight: 600; 46 | margin-top: 4px; 47 | } 48 | 49 | .signallet-section { 50 | margin-bottom: 16px; 51 | } 52 | 53 | .signallet-section:last-child { 54 | margin-bottom: 0; 55 | } 56 | 57 | .signallet-signals { 58 | display: flex; 59 | flex-direction: column; 60 | gap: 8px; 61 | } 62 | 63 | .signal-item { 64 | padding: 12px; 65 | border: 1px solid #eee; 66 | border-radius: 6px; 67 | } 68 | 69 | .signal-header { 70 | display: flex; 71 | justify-content: space-between; 72 | align-items: center; 73 | margin-bottom: 8px; 74 | } 75 | 76 | .signal-symbol { 77 | font-weight: 600; 78 | font-size: 14px; 79 | } 80 | 81 | .signal-tag { 82 | font-size: 12px; 83 | padding: 2px 6px; 84 | border-radius: 4px; 85 | background: #e6f7ff; 86 | color: #0066cc; 87 | } 88 | 89 | .signal-tag.BUY_OPEN { 90 | background: #f6ffed; 91 | color: #52c41a; 92 | } 93 | 94 | .signal-tag.SELL_OPEN { 95 | background: #fff1f0; 96 | color: #f5222d; 97 | } 98 | 99 | .signal-details { 100 | display: flex; 101 | justify-content: space-between; 102 | font-size: 12px; 103 | } 104 | 105 | .signal-info { 106 | display: flex; 107 | gap: 8px; 108 | color: #666; 109 | } 110 | 111 | .signal-status { 112 | display: flex; 113 | gap: 8px; 114 | align-items: center; 115 | } 116 | 117 | .status-tag { 118 | padding: 2px 6px; 119 | border-radius: 4px; 120 | background: #f5f5f5; 121 | color: #666; 122 | } 123 | 124 | .status-tag.open { 125 | background: #e6f7ff; 126 | color: #0066cc; 127 | } 128 | 129 | .status-tag.closed { 130 | background: #f5f5f5; 131 | color: #666; 132 | } 133 | 134 | .profit { 135 | color: #52c41a; 136 | } 137 | 138 | .loss { 139 | color: #f5222d; 140 | } -------------------------------------------------------------------------------- /frontend/src/components/Toast.css: -------------------------------------------------------------------------------- 1 | .toast { 2 | position: fixed; 3 | top: 16px; 4 | right: 16px; 5 | padding: 12px 24px; 6 | border-radius: 4px; 7 | background: white; 8 | box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05); 9 | display: flex; 10 | align-items: center; 11 | gap: 12px; 12 | z-index: 1000; 13 | animation: slideIn 0.3s ease-out; 14 | } 15 | 16 | @keyframes slideIn { 17 | from { 18 | transform: translateX(100%); 19 | opacity: 0; 20 | } 21 | to { 22 | transform: translateX(0); 23 | opacity: 1; 24 | } 25 | } 26 | 27 | .toast.success { 28 | border-left: 4px solid #52c41a; 29 | } 30 | 31 | .toast.error { 32 | border-left: 4px solid #f5222d; 33 | } 34 | 35 | .toast.info { 36 | border-left: 4px solid #1890ff; 37 | } 38 | 39 | .toast-content { 40 | display: flex; 41 | align-items: center; 42 | gap: 8px; 43 | } 44 | 45 | .toast-content svg { 46 | font-size: 16px; 47 | } 48 | 49 | .toast.success svg { 50 | color: #52c41a; 51 | } 52 | 53 | .toast.error svg { 54 | color: #f5222d; 55 | } 56 | 57 | .toast.info svg { 58 | color: #1890ff; 59 | } 60 | 61 | .toast-close { 62 | background: none; 63 | border: none; 64 | padding: 4px; 65 | cursor: pointer; 66 | color: #999; 67 | display: flex; 68 | align-items: center; 69 | justify-content: center; 70 | transition: color 0.3s; 71 | } 72 | 73 | .toast-close:hover { 74 | color: #666; 75 | } 76 | 77 | .toast-close svg { 78 | font-size: 14px; 79 | } -------------------------------------------------------------------------------- /frontend/src/components/Toast.tsx: -------------------------------------------------------------------------------- 1 | import React, { useEffect } from 'react'; 2 | import './Toast.css'; 3 | 4 | interface ToastProps { 5 | message: string; 6 | type: 'success' | 'error' | 'info'; 7 | onClose: () => void; 8 | duration?: number; 9 | } 10 | 11 | const Toast: React.FC = ({ message, type, onClose, duration = 3000 }) => { 12 | useEffect(() => { 13 | const timer = setTimeout(() => { 14 | onClose(); 15 | }, duration); 16 | 17 | return () => clearTimeout(timer); 18 | }, [duration, onClose]); 19 | 20 | return ( 21 |
22 |
23 | {type === 'success' && ( 24 | 27 | )} 28 | {type === 'error' && ( 29 | 32 | )} 33 | {type === 'info' && ( 34 | 37 | )} 38 | {message} 39 |
40 | 45 |
46 | ); 47 | }; 48 | 49 | export default Toast; -------------------------------------------------------------------------------- /frontend/src/components/home/FuturesKLineChart.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import ReactECharts from 'echarts-for-react'; 3 | 4 | interface FuturesData { 5 | ts_code: string; 6 | trade_date: string; 7 | open: number; 8 | high: number; 9 | low: number; 10 | close: number; 11 | vol: number; 12 | amount: number; 13 | } 14 | 15 | interface FuturesKLineChartProps { 16 | data: FuturesData[] | undefined; 17 | } 18 | 19 | const FuturesKLineChart: React.FC = ({ data }) => { 20 | const getChartOption = () => { 21 | if (!data) return {}; 22 | 23 | // 按合约分组数据 24 | const contractData = data.reduce((acc, item) => { 25 | if (!acc[item.ts_code]) { 26 | acc[item.ts_code] = []; 27 | } 28 | acc[item.ts_code].push([ 29 | item.trade_date, 30 | item.open, 31 | item.close, 32 | item.low, 33 | item.high 34 | ]); 35 | return acc; 36 | }, {} as Record); 37 | 38 | const series = Object.entries(contractData).map(([contract, data]) => ({ 39 | name: contract, 40 | type: 'candlestick', 41 | data: data, 42 | itemStyle: { 43 | color: '#ef5350', 44 | color0: '#26a69a', 45 | borderColor: '#ef5350', 46 | borderColor0: '#26a69a' 47 | }, 48 | encode: { 49 | x: 0, 50 | y: [1, 2, 3, 4] 51 | } 52 | })); 53 | 54 | return { 55 | tooltip: { 56 | trigger: 'axis', 57 | axisPointer: { 58 | type: 'cross' 59 | } 60 | }, 61 | legend: { 62 | data: Object.keys(contractData) 63 | }, 64 | grid: { 65 | left: '10%', 66 | right: '10%', 67 | bottom: '15%' 68 | }, 69 | xAxis: { 70 | type: 'category', 71 | data: Array.from(new Set(data.map(item => item.trade_date))), 72 | scale: true, 73 | boundaryGap: false, 74 | axisLine: { onZero: false }, 75 | splitLine: { show: false }, 76 | splitNumber: 20 77 | }, 78 | yAxis: { 79 | scale: true, 80 | splitArea: { 81 | show: true 82 | } 83 | }, 84 | dataZoom: [ 85 | { 86 | type: 'inside', 87 | start: 50, 88 | end: 100 89 | }, 90 | { 91 | show: true, 92 | type: 'slider', 93 | bottom: '5%', 94 | start: 50, 95 | end: 100 96 | } 97 | ], 98 | series 99 | }; 100 | }; 101 | 102 | return ( 103 |
104 |
105 |
106 |

历史K线图表

107 |

108 | 选择的时间范围内豆粕期货价格走势 109 |

110 |
111 | 112 |
113 | 118 |
119 |
120 |
121 | ); 122 | }; 123 | 124 | export default FuturesKLineChart; -------------------------------------------------------------------------------- /frontend/src/components/home/InventoryInfo.tsx: -------------------------------------------------------------------------------- 1 | import React, { useEffect, useRef } from 'react'; 2 | import * as echarts from 'echarts'; 3 | 4 | interface InventoryData { 5 | total_inventory: number; 6 | warehouse_inventory: number; 7 | port_inventory: number; 8 | update_date: string; 9 | history_data?: { 10 | date: string; 11 | inventory: number; 12 | }[]; 13 | } 14 | 15 | interface InventoryInfoProps { 16 | data: InventoryData | undefined; 17 | } 18 | 19 | const InventoryInfo: React.FC = ({ data }) => { 20 | const chartRef = useRef(null); 21 | const chartInstance = useRef(null); 22 | 23 | useEffect(() => { 24 | if (!chartRef.current || !data?.history_data) return; 25 | 26 | if (!chartInstance.current) { 27 | chartInstance.current = echarts.init(chartRef.current); 28 | } 29 | 30 | const option = { 31 | grid: { 32 | top: 25, 33 | right: 5, 34 | bottom: 25, 35 | left: 5, 36 | containLabel: true 37 | }, 38 | tooltip: { 39 | trigger: 'axis', 40 | axisPointer: { 41 | type: 'shadow' 42 | }, 43 | formatter: (params: any) => { 44 | const data = params[0]; 45 | return `${data.name}
库存: ${data.value.toLocaleString()} 吨`; 46 | } 47 | }, 48 | xAxis: { 49 | type: 'category', 50 | show: true, 51 | data: data.history_data.map(item => { 52 | const date = new Date(item.date); 53 | return `${(date.getMonth() + 1).toString().padStart(2, '0')}-${date.getDate().toString().padStart(2, '0')}`; 54 | }), 55 | axisLabel: { 56 | fontSize: 10, 57 | color: '#666' 58 | } 59 | }, 60 | yAxis: { 61 | type: 'value', 62 | show: true, 63 | min: 'dataMin', 64 | max: 'dataMax', 65 | axisLabel: { 66 | fontSize: 10, 67 | color: '#666', 68 | formatter: (value: number) => { 69 | return (value / 1000).toFixed(1) + 'k'; 70 | } 71 | } 72 | }, 73 | series: [{ 74 | data: data.history_data.map(item => item.inventory), 75 | type: 'bar', 76 | barWidth: '80%', 77 | itemStyle: { 78 | color: '#3B82F6' 79 | } 80 | }] 81 | }; 82 | 83 | chartInstance.current.setOption(option); 84 | 85 | const handleResize = () => { 86 | chartInstance.current?.resize(); 87 | }; 88 | 89 | window.addEventListener('resize', handleResize); 90 | 91 | return () => { 92 | window.removeEventListener('resize', handleResize); 93 | chartInstance.current?.dispose(); 94 | chartInstance.current = null; 95 | }; 96 | }, [data]); 97 | 98 | return ( 99 |
100 |
101 |
102 |

库存信息

103 |

104 | 实时获取豆粕期货库存信息 105 |

106 |
107 | 108 |
109 |
110 |

总库存

111 |

112 | {data?.total_inventory.toLocaleString() || '-'} 吨 113 |

114 | {data?.history_data && ( 115 |
116 | )} 117 |
118 |
119 |

仓库库存

120 |

121 | {data?.warehouse_inventory.toLocaleString() || '-'} 吨 122 |

123 |
124 |
125 |

港口库存

126 |

127 | {data?.port_inventory.toLocaleString() || '-'} 吨 128 |

129 |
130 |
131 |
132 |
133 | ); 134 | }; 135 | 136 | export default InventoryInfo; -------------------------------------------------------------------------------- /frontend/src/components/home/TimeRangeSelector.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | 3 | interface TimeRangeSelectorProps { 4 | timeRange: '1m' | '3m' | '6m' | '1y'; 5 | onTimeRangeChange: (range: '1m' | '3m' | '6m' | '1y') => void; 6 | } 7 | 8 | const TimeRangeSelector: React.FC = ({ timeRange, onTimeRangeChange }) => { 9 | return ( 10 |
11 |
12 |
13 |

时间范围

14 |

15 | 选择要查看的时间范围 16 |

17 |
18 | 19 |
20 | 28 | 36 | 44 | 52 |
53 |
54 |
55 | ); 56 | }; 57 | 58 | export default TimeRangeSelector; -------------------------------------------------------------------------------- /frontend/src/components/layout/Footer.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import { Link, NavLink } from 'react-router-dom'; 3 | 4 | const Footer: React.FC = () => { 5 | // 定义活动链接的类名函数 6 | const getLegalLinkClass = ({ isActive }: { isActive: boolean }) => { 7 | return isActive 8 | ? "text-white font-medium text-sm underline" 9 | : "text-gray-400 hover:text-white text-sm"; 10 | }; 11 | 12 | return ( 13 |
14 |
15 |
16 | {/* Company Info */} 17 |
18 |

新致量化策略

19 |

20 | 本站是豆粕品种量化交易策略平台，为您提供全方位的市场数据分析和交易策略服务。 21 |

22 |
23 | 24 | {/* Quick Links */} 25 |
26 |

快速链接

27 | 64 |
65 | 66 | {/* Legal */} 67 |
68 |

法律信息

69 |
    70 |
  • 71 | 72 | 隐私政策 73 | 74 |
    • 75 |
  • 76 | 77 | 服务条款 78 | 79 |
    • 80 |
  • 81 | 82 | 免责声明 83 | 84 |
    • 85 |
86 |
87 | 88 | {/* Contact */} 89 |
90 |

联系我们

91 |
    92 |
  • 93 | 邮箱：contact@singzquant.com 94 |
    • 95 |
96 |
97 |
98 | 99 | {/* Copyright */} 100 |
101 |

© {new Date().getFullYear()} 新致智能量化. All rights reserved.

102 |

103 | 109 | 苏ICP备2025174962号 110 | 111 |

112 |
113 |
114 |
115 | ); 116 | }; 117 | 118 | export default Footer; -------------------------------------------------------------------------------- /frontend/src/components/layout/Layout.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import Header from './Header'; 3 | import Footer from './Footer'; 4 | 5 | interface LayoutProps { 6 | children: React.ReactNode; 7 | } 8 | 9 | const Layout: React.FC = ({ children }) => { 10 | return ( 11 |
12 |
13 |
14 | {children} 15 |
16 |
17 |
18 | ); 19 | }; 20 | 21 | export default Layout; -------------------------------------------------------------------------------- /frontend/src/components/market/HistoricalComparison.tsx: -------------------------------------------------------------------------------- 1 | import React, { useMemo } from 'react'; 2 | import ReactECharts from 'echarts-for-react'; 3 | import { Card, Button, Tag } from 'antd'; 4 | import { ContractPrice } from '../../types/market'; 5 | import dayjs from 'dayjs'; 6 | import { Link } from 'react-router-dom'; 7 | import { CrownOutlined } from '@ant-design/icons'; 8 | 9 | interface HistoricalComparisonProps { 10 | currentContract: ContractPrice; 11 | historicalContracts: ContractPrice[]; 12 | } 13 | 14 | const HistoricalComparison: React.FC = ({ 15 | currentContract, 16 | historicalContracts, 17 | }) => { 18 | const option = useMemo(() => { 19 | // 获取当前合约的月份 20 | const currentMonth = currentContract.contract.slice(-2); 21 | 22 | // 对当前合约数据进行处理 23 | const currentPrices = currentContract.historicalPrices 24 | .sort((a, b) => dayjs(a.date).diff(dayjs(b.date))) 25 | .map(price => [ 26 | dayjs(price.date).format('MM-DD'), 27 | price.close 28 | ]); 29 | 30 | const historicalSeries = historicalContracts 31 | .filter(contract => { 32 | // 只选择相同月份的合约 33 | const contractMonth = contract.contract.slice(-2); 34 | return contractMonth === currentMonth; 35 | }) 36 | .map((contract) => { 37 | const prices = contract.historicalPrices 38 | .sort((a, b) => dayjs(a.date).diff(dayjs(b.date))) 39 | .map(price => [ 40 | dayjs(price.date).format('MM-DD'), 41 | price.close 42 | ]); 43 | 44 | // 从合约代码中提取年份 45 | const year = contract.contract.slice(-4, -2); 46 | 47 | return { 48 | name: `20${year}年`, 49 | type: 'line', 50 | data: prices, 51 | lineStyle: { 52 | opacity: 0.3 53 | } 54 | }; 55 | }) 56 | .filter(series => series.data.length > 0); 57 | 58 | // 获取所有日期并排序 59 | const allDates = Array.from(new Set([ 60 | ...currentPrices.map(p => p[0]), 61 | ...historicalSeries.flatMap(s => s.data.map(d => d[0])) 62 | ])).sort((a, b) => { 63 | // 将MM-DD转换为虚拟的完整日期进行排序 64 | const [monthA, dayA] = String(a).split('-').map(Number); 65 | const [monthB, dayB] = String(b).split('-').map(Number); 66 | 67 | // 如果月份大于等于9，使用前一年，这样9-12月会排在1-8月前面 68 | const yearA = monthA >= 9 ? 2000 : 2001; 69 | const yearB = monthB >= 9 ? 2000 : 2001; 70 | 71 | const dateA = new Date(yearA, monthA - 1, dayA); 72 | const dateB = new Date(yearB, monthB - 1, dayB); 73 | 74 | return dateA.getTime() - dateB.getTime(); 75 | }); 76 | 77 | return { 78 | title: { 79 | text: `主力合约历史走势比对`, 80 | left: 'center' 81 | }, 82 | tooltip: { 83 | trigger: 'axis', 84 | formatter: (params: any) => { 85 | let result = `${params[0].axisValue}
`; 86 | params.forEach((param: any) => { 87 | result += `${param.seriesName}: ${param.value[1].toFixed(2)}
`; 88 | }); 89 | return result; 90 | } 91 | }, 92 | dataZoom: [ 93 | { 94 | type: 'slider', 95 | show: true, 96 | xAxisIndex: [0], 97 | start: 0, 98 | end: 100, 99 | bottom: 60 100 | }, 101 | { 102 | type: 'inside', 103 | xAxisIndex: [0], 104 | start: 0, 105 | end: 100 106 | } 107 | ], 108 | legend: { 109 | data: [`${currentContract.contract}(当前)`, ...historicalSeries.map(s => s.name)], 110 | bottom: 30 111 | }, 112 | grid: { 113 | left: '3%', 114 | right: '4%', 115 | bottom: '25%', 116 | top: '10%', 117 | containLabel: true 118 | }, 119 | xAxis: { 120 | type: 'category', 121 | boundaryGap: false, 122 | data: allDates, 123 | axisLabel: { 124 | formatter: (value: string) => value 125 | } 126 | }, 127 | yAxis: { 128 | type: 'value', 129 | scale: true 130 | }, 131 | series: [ 132 | { 133 | name: `${currentContract.contract}(当前)`, 134 | type: 'line', 135 | data: currentPrices, 136 | lineStyle: { 137 | width: 2 138 | }, 139 | emphasis: { 140 | lineStyle: { 141 | width: 3 142 | } 143 | }, 144 | smooth: true, 145 | showSymbol: false 146 | }, 147 | ...historicalSeries.map(series => ({ 148 | ...series, 149 | smooth: true, 150 | showSymbol: false 151 | })) 152 | ] 153 | }; 154 | }, [currentContract, historicalContracts]); 155 | 156 | return ( 157 | 161 | 历史走势比对 162 | window.location.href = '/pro-analysis'} 165 | > 166 | PRO 167 | 168 |
169 | } 170 | > 171 | 175 | 176 | ); 177 | }; 178 | 179 | export default HistoricalComparison; -------------------------------------------------------------------------------- /frontend/src/components/market/MarketOverview.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import { useQuery } from '@tanstack/react-query'; 3 | import axios from 'axios'; 4 | import { API_ENDPOINTS } from '../../config/api'; 5 | 6 | interface MarketData { 7 | trade_date: string; 8 | close: number; 9 | change1: number; 10 | vol: number; 11 | amount: number; 12 | } 13 | 14 | const SkeletonCard = () => ( 15 |
16 |
17 |
18 |
19 | ); 20 | 21 | const MarketOverview: React.FC = () => { 22 | const { data: marketData, isLoading } = useQuery({ 23 | queryKey: ['marketData'], 24 | queryFn: async () => { 25 | const response = await axios.get(API_ENDPOINTS.market.futures); 26 | return response.data; 27 | }, 28 | refetchOnWindowFocus: false 29 | }); 30 | 31 | const latestData = marketData?.[marketData.length - 1]; 32 | const pctChange = latestData?.change1 ?? 0; 33 | 34 | if (isLoading) { 35 | return ( 36 |
37 |
38 |
39 | 40 | 41 | 42 | 43 |
44 |
45 | ); 46 | } 47 | 48 | return ( 49 |
50 |

市场概览（主力合约）

51 |
52 |
53 |

最新价格

54 |

55 | ¥{latestData?.close?.toFixed(2) ?? '0.00'} 56 |

57 |
58 |
59 |

涨跌额（收盘价-昨结算价）

60 |

61 | {pctChange} 62 |

63 |
64 |
65 |

成交量

66 |

67 | {(latestData?.vol ?? 0).toLocaleString()} 68 |

69 |
70 |
71 |

成交额（万）

72 |

73 | ¥{(latestData?.amount ?? 0).toLocaleString()} 74 |

75 |
76 |
77 |
78 | ); 79 | }; 80 | 81 | export default MarketOverview; -------------------------------------------------------------------------------- /frontend/src/components/market/PriceChart.tsx: -------------------------------------------------------------------------------- 1 | import React, { useEffect, useRef } from 'react'; 2 | import * as echarts from 'echarts'; 3 | import { ContractPrice } from '../../types/market'; 4 | 5 | interface PriceChartProps { 6 | contract: ContractPrice; 7 | } 8 | 9 | const PriceChart: React.FC = ({ contract }) => { 10 | const chartRef = useRef(null); 11 | 12 | useEffect(() => { 13 | if (!chartRef.current) return; 14 | 15 | // 获取历史数据 16 | const historicalData = contract.historicalPrices 17 | .filter(item => item.contract === contract.contract) 18 | .sort((a, b) => new Date(a.date).getTime() - new Date(b.date).getTime()); 19 | 20 | if (historicalData.length === 0) return; 21 | 22 | // 提取日期和价格数据 23 | const dates = historicalData.map(item => item.date); 24 | const prices = historicalData.map(item => item.close); 25 | 26 | // 创建ECharts实例 27 | const chartInstance = echarts.init(chartRef.current); 28 | 29 | // 图表配置项 30 | const option = { 31 | tooltip: { 32 | trigger: 'axis', 33 | formatter: function(params: any) { 34 | const historicalPoint = params[0]; 35 | const currentPoint = params[1]; 36 | return ` 37 |
38 |

${historicalPoint.axisValue}

39 |

40 | 41 | 主力合约收盘价格: ${historicalPoint.value.toFixed(2)}元/吨 42 |

43 |

44 | 45 | 当前价格: ${contract.price.toFixed(2)}元/吨 46 |

47 |
48 | `; 49 | } 50 | }, 51 | legend: { 52 | data: ['主力合约收盘价格', '当前价格'], 53 | top: 0 54 | }, 55 | grid: { 56 | left: '3%', 57 | right: '4%', 58 | bottom: '3%', 59 | containLabel: true 60 | }, 61 | xAxis: { 62 | type: 'category', 63 | boundaryGap: false, 64 | data: dates, 65 | name: '日期', 66 | nameLocation: 'middle', 67 | nameGap: 30 68 | }, 69 | yAxis: { 70 | type: 'value', 71 | name: '价格 (元/吨)', 72 | nameLocation: 'middle', 73 | nameGap: 50, 74 | axisLabel: { 75 | formatter: '{value}.00' 76 | }, 77 | min: function(value: any) { 78 | // 设置最小值为最低价格的95% 79 | return Math.floor(value.min * 0.95); 80 | }, 81 | max: function(value: any) { 82 | // 设置最大值为最高价格的105% 83 | return Math.ceil(value.max * 1.05); 84 | }, 85 | splitNumber: 5, // 设置分割段数 86 | scale: true, // 启用自动缩放 87 | boundaryGap: [0.1, 0.1] // 设置边界留白比例 88 | }, 89 | series: [ 90 | { 91 | name: '主力合约收盘价格', 92 | type: 'line', 93 | data: prices, 94 | smooth: true, 95 | symbol: 'none', 96 | lineStyle: { 97 | width: 2, 98 | color: '#1890ff' 99 | }, 100 | itemStyle: { 101 | color: '#1890ff' 102 | }, 103 | connectNulls: true, 104 | emphasis: { 105 | focus: 'series' 106 | } 107 | }, 108 | { 109 | name: '当前价格', 110 | type: 'line', 111 | symbol: 'none', 112 | smooth: true, 113 | data: Array(dates.length).fill(parseFloat(contract.price.toString())), 114 | lineStyle: { 115 | width: 3, 116 | color: '#FF4D4F', 117 | type: 'dashed' 118 | }, 119 | connectNulls: true, 120 | emphasis: { 121 | focus: 'series' 122 | } 123 | } 124 | ] 125 | }; 126 | 127 | // 渲染图表 128 | chartInstance.setOption(option); 129 | 130 | // 窗口大小变化时重绘图表 131 | const handleResize = () => { 132 | chartInstance.resize(); 133 | }; 134 | window.addEventListener('resize', handleResize); 135 | 136 | return () => { 137 | window.removeEventListener('resize', handleResize); 138 | chartInstance.dispose(); 139 | }; 140 | }, [contract]); 141 | 142 | return ( 143 |
144 |

价格走势

145 |
146 |
147 | ); 148 | }; 149 | 150 | export default PriceChart; -------------------------------------------------------------------------------- /frontend/src/components/trading/StrategyAdvice.tsx: -------------------------------------------------------------------------------- 1 | import React, { useState } from 'react'; 2 | import ReactMarkdown from 'react-markdown'; 3 | import remarkGfm from 'remark-gfm'; 4 | import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter'; 5 | import { vscDarkPlus } from 'react-syntax-highlighter/dist/esm/styles/prism'; 6 | import type { Components } from 'react-markdown'; 7 | 8 | interface StrategyResponse { 9 | content: string; 10 | reasoning_content: string; 11 | } 12 | 13 | interface CodeProps { 14 | node?: any; 15 | inline?: boolean; 16 | className?: string; 17 | children?: React.ReactNode; 18 | [key: string]: any; 19 | } 20 | 21 | interface StrategyAdviceProps { 22 | strategyData: StrategyResponse | null; 23 | isStreaming: boolean; 24 | } 25 | 26 | const StrategyAdvice: React.FC = ({ strategyData, isStreaming }) => { 27 | const [showReasoning, setShowReasoning] = useState(false); 28 | 29 | const markdownComponents: Components = { 30 | code({ node, inline, className, children, ...props }: CodeProps) { 31 | const match = /language-(\w+)/.exec(className || ''); 32 | return !inline && match ? ( 33 | 39 | {String(children).replace(/\n$/, '')} 40 | 41 | ) : ( 42 | 43 | {children} 44 | 45 | ); 46 | }, 47 | h1: ({ children }) =>

{children}

, 48 | h2: ({ children }) =>

{children}

, 49 | h3: ({ children }) =>

{children}

, 50 | p: ({ children }) =>

{children}

, 51 | ul: ({ children }) =>
    {children}
, 52 | ol: ({ children }) =>
    {children}
, 53 | li: ({ children }) =>
  • {children}
    • , 54 | blockquote: ({ children }) => ( 55 |
    56 | {children} 57 |
    58 | ), 59 | table: ({ children }) => ( 60 |
    61 | 62 | {children} 63 |
    64 |
    65 | ), 66 | th: ({ children }) => ( 67 | 68 | {children} 69 | 70 | ), 71 | td: ({ children }) => ( 72 | 73 | {children} 74 | 75 | ), 76 | }; 77 | 78 | if (!strategyData) return null; 79 | 80 | return ( 81 |
    82 |
    83 |

    交易策略建议

    84 |

    基于市场分析的交易策略建议

    85 |
    86 | 87 |
    88 |
    89 |
    90 | {/*
    91 | {!isStreaming && strategyData.reasoning_content && ( 92 | 98 | )} 99 |
    */} 100 |
    101 | 105 | {strategyData.content} 106 | 107 |
    108 | 109 | {/* 思维链分析 */} 110 | {!isStreaming && strategyData.reasoning_content && ( 111 |
    114 |
    115 |

    思维链分析

    116 |
    117 | 121 | {strategyData.reasoning_content} 122 | 123 |
    124 |
    125 |
    126 | )} 127 |
    128 |
    129 |
    130 |
    131 | ); 132 | }; 133 | 134 | export default StrategyAdvice; -------------------------------------------------------------------------------- /frontend/src/config/api.ts: -------------------------------------------------------------------------------- 1 | // 根据环境设置API基础URL 2 | const isDevelopment = process.env.NODE_ENV === 'development'; 3 | export const API_BASE_URL = isDevelopment 4 | ? 'http://localhost:8000/api/v1' 5 | : '/api/v1'; 6 | 7 | export const API_ENDPOINTS = { 8 | market: { 9 | kline: (period: string) => `${API_BASE_URL}/market/kline/${period}`, 10 | futures: `${API_BASE_URL}/market/futures`, 11 | etf: `${API_BASE_URL}/market/etf`, 12 | options: `${API_BASE_URL}/market/options`, 13 | optionsHedge: `${API_BASE_URL}/market/options/hedge`, 14 | futuresContracts: `${API_BASE_URL}/market/futures/contracts`, 15 | futuresInventory: `${API_BASE_URL}/market/futures/inventory`, 16 | inventory: `${API_BASE_URL}/market/inventory`, 17 | technical: `${API_BASE_URL}/market/technical`, 18 | historical: `${API_BASE_URL}/market/futures/historical`, 19 | monthlyProbability: `${API_BASE_URL}/market/futures/monthly-probability`, 20 | costComparison: `${API_BASE_URL}/market/futures/cost-comparison`, 21 | supportResistance: `${API_BASE_URL}/market/support-resistance` 22 | }, 23 | analysis: { 24 | fundamental: `${API_BASE_URL}/fundamental/analysis`, 25 | supplyDemand: `${API_BASE_URL}/fundamental/supply-demand`, 26 | seasonal: `${API_BASE_URL}/fundamental/seasonal`, 27 | weather: `${API_BASE_URL}/fundamental/weather`, 28 | crushProfit: `${API_BASE_URL}/fundamental/crush-profit`, 29 | overall: `${API_BASE_URL}/fundamental/overall`, 30 | coreFactor: `${API_BASE_URL}/core-factor` 31 | }, 32 | model: { 33 | predict: `${API_BASE_URL}/model/predict` 34 | }, 35 | trading: { 36 | options: `${API_BASE_URL}/trading/options` 37 | }, 38 | trendFollow: { 39 | data15min: `${API_BASE_URL}/trend_follow/15min`, 40 | data60min: `${API_BASE_URL}/trend_follow/60min`, 41 | backtest: `${API_BASE_URL}/trend_follow/backtest` 42 | }, 43 | obvAdxEma: { 44 | data: `${API_BASE_URL}/obv_adx_ema/data`, 45 | backtest: `${API_BASE_URL}/obv_adx_ema/backtest` 46 | }, 47 | dualma: { 48 | weekly: `${API_BASE_URL}/dual_ma/weekly`, 49 | backtest: `${API_BASE_URL}/dual_ma/backtest` 50 | }, 51 | grid: { 52 | data: `${API_BASE_URL}/grid/data`, 53 | backtest: `${API_BASE_URL}/grid/backtest`, 54 | }, 55 | news: { 56 | daily: `${API_BASE_URL}/news/daily`, 57 | analysis: `${API_BASE_URL}/news/analysis`, 58 | analyze: `${API_BASE_URL}/news/analyze` 59 | }, 60 | supportResistance: { 61 | data: `${API_BASE_URL}/support_resistance/data`, 62 | backtest: `${API_BASE_URL}/support_resistance/backtest` 63 | }, 64 | stockFutures: { 65 | stockPicking: `${API_BASE_URL}/stockfutures/stock-picking` 66 | } 67 | }; -------------------------------------------------------------------------------- /frontend/src/index.css: -------------------------------------------------------------------------------- 1 | @tailwind base; 2 | @tailwind components; 3 | @tailwind utilities; 4 | 5 | body { 6 | margin: 0; 7 | font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 8 | 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', 9 | sans-serif; 10 | -webkit-font-smoothing: antialiased; 11 | -moz-osx-font-smoothing: grayscale; 12 | } 13 | 14 | code { 15 | font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New', 16 | monospace; 17 | } -------------------------------------------------------------------------------- /frontend/src/index.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import ReactDOM from 'react-dom/client'; 3 | import './index.css'; 4 | import App from './App'; 5 | 6 | const root = ReactDOM.createRoot( 7 | document.getElementById('root') as HTMLElement 8 | ); 9 | 10 | root.render( 11 | 12 | ); -------------------------------------------------------------------------------- /frontend/src/pages/CoreFactorAnalysisPage.tsx: -------------------------------------------------------------------------------- 1 | import React, { useState, useEffect } from 'react'; 2 | import Layout from '../components/layout/Layout'; 3 | import CoreFactorAnalysis from '../components/analysis/CoreFactorAnalysis'; 4 | import axios from 'axios'; 5 | import { API_ENDPOINTS } from '../config/api'; 6 | 7 | const CoreFactorAnalysisPage: React.FC = () => { 8 | const minDate = "2024-01-01"; 9 | const today = new Date().toISOString().split('T')[0]; 10 | const initialDate = today; 11 | 12 | const [selectedDate, setSelectedDate] = useState(initialDate); 13 | const [isLoading, setIsLoading] = useState(false); 14 | const [factorData, setFactorData] = useState(null); 15 | 16 | // 获取核心驱动因子数据 17 | const fetchFactorData = async () => { 18 | setIsLoading(true); 19 | try { 20 | const response = await axios.get(`${API_ENDPOINTS.analysis.coreFactor}/${selectedDate}`); 21 | setFactorData(response.data); 22 | } catch (error) { 23 | console.error('获取核心驱动因子数据失败:', error); 24 | } finally { 25 | setIsLoading(false); 26 | } 27 | }; 28 | 29 | // 页面加载时调用一次获取数据接口 30 | useEffect(() => { 31 | fetchFactorData(); 32 | }, []); 33 | 34 | return ( 35 | 36 |
    37 |
    38 |

    39 | 核心驱动因子分析 40 |

    41 |

    42 | 豆粕市场核心驱动因子深度分析 43 |

    44 |
    45 | 46 | {/* 日期选择和查看分析按钮 */} 47 |
    48 |
    49 |
    50 | 53 | setSelectedDate(e.target.value)} 59 | min={minDate} 60 | max={today} 61 | /> 62 |
    63 |
    64 | 71 |
    72 |
    73 |
    74 | 75 | 76 |
    77 |     78 | ); 79 | }; 80 | 81 | export default CoreFactorAnalysisPage; -------------------------------------------------------------------------------- /frontend/src/pages/Disclaimer.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import Layout from '../components/layout/Layout'; 3 | 4 | const Disclaimer: React.FC = () => { 5 | return ( 6 | 7 |
    8 |

    免责声明

    9 | 10 |
    11 |
    12 |

    1. 信息用途

    13 |

    14 | 新致量化策略分析平台（以下简称"本平台"）提供的所有信息、分析、策略建议和其他内容仅供参考，不构成投资建议或交易指导。用户在做出任何投资决策前，应当咨询专业金融顾问。 15 |

    16 |
    17 | 18 |
    19 |

    2. 风险提示

    20 |

    21 | 股票、期货、基金和衍生品交易涉及重大风险，可能导致严重的财务损失。本平台提供的策略和分析不保证能获得投资收益或避免损失。用户在交易前应充分了解相关风险，并根据自身财务状况和风险承受能力作出决策。 22 |

    23 |
    24 | 25 |
    26 |

    3. 内容准确性

    27 |

    28 | 尽管我们努力确保本平台提供的信息准确可靠，但我们不对信息的准确性、完整性、时效性或适用性做出任何保证。市场情况瞬息万变，任何分析和预测可能会因诸多因素而失效。 29 |

    30 |
    31 | 32 |
    33 |

    4. 技术风险

    34 |

    35 | 使用本平台涉及互联网和电子系统的使用，可能会受到硬件故障、软件问题、连接中断、系统延迟等技术因素的影响。用户应当意识到这些风险，并准备好应对可能出现的问题。 36 |

    37 |
    38 | 39 |
    40 |

    5. 第三方内容

    41 |

    42 | 本平台可能包含来自第三方的内容或链接到第三方网站。我们不对这些第三方内容的准确性或可靠性负责，也不对用户访问这些链接可能造成的任何损失或损害承担责任。 43 |

    44 |
    45 | 46 |
    47 |

    6. 模型和算法局限性

    48 |

    49 | 本平台使用的分析模型和算法基于历史数据和特定假设，存在固有局限性。市场行为可能会偏离历史模式，导致模型预测失效。用户不应完全依赖这些模型做出决策。 50 |

    51 |
    52 | 53 |
    54 |

    7. 责任限制

    55 |

    56 | 在法律允许的最大范围内，本平台及其运营者、员工、合作伙伴不对用户因使用或依赖本平台提供的信息而产生的任何直接、间接、附带、特殊或后果性损害承担责任，包括但不限于财务损失、利润损失、业务中断等。 57 |

    58 |
    59 | 60 |
    61 |

    8. 合规责任

    62 |

    63 | 用户应当了解并遵守与股票、期货、基金和衍生品交易相关的所有适用法律、法规和监管要求。本平台不对用户的交易行为是否符合相关法律法规负责，用户应自行承担合规责任。 64 |

    65 |
    66 | 67 |
    68 |

    9. 资格要求

    69 |

    70 | 用户确认其具备使用本平台服务和进行相关交易的法律资格，包括但不限于达到法定年龄、具备足够的风险识别和承担能力等。 71 |

    72 |
    73 | 74 |
    75 |

    10. 同意条款

    76 |

    77 | 使用本平台即表示您已阅读、理解并接受本免责声明的所有条款。如果您不同意这些条款，请勿使用本平台及其提供的服务。 78 |

    79 |
    80 | 81 |
    82 | 最后更新日期：2025年3月30日 83 |
    84 |
    85 |
    86 |     87 | ); 88 | }; 89 | 90 | export default Disclaimer; -------------------------------------------------------------------------------- /frontend/src/pages/OptionsStrategyPage.tsx: -------------------------------------------------------------------------------- 1 | import React, { useState, useEffect } from 'react'; 2 | import Layout from '../components/layout/Layout'; 3 | import OptionsStrategy from '../components/trading/OptionsStrategy'; 4 | import axios from 'axios'; 5 | import { API_ENDPOINTS } from '../config/api'; 6 | 7 | const OptionsStrategyPage: React.FC = () => { 8 | const minDate = "2025-04-10"; 9 | const today = new Date().toISOString().split('T')[0]; 10 | const initialDate = today < minDate ? minDate : today; 11 | 12 | const [selectedDate, setSelectedDate] = useState(initialDate); 13 | const [isLoading, setIsLoading] = useState(false); 14 | const [fundamentalData, setFundamentalData] = useState(null); 15 | 16 | // 获取基本面数据 17 | const fetchFundamentalData = async () => { 18 | setIsLoading(true); 19 | try { 20 | const response = await axios.get(`${API_ENDPOINTS.analysis.fundamental}`, { 21 | params: { date: selectedDate } 22 | }); 23 | setFundamentalData(response.data); 24 | } catch (error) { 25 | console.error('获取基本面数据失败:', error); 26 | } finally { 27 | setIsLoading(false); 28 | } 29 | }; 30 | 31 | // 页面加载时调用一次获取基本面数据 32 | useEffect(() => { 33 | fetchFundamentalData(); 34 | }, []); 35 | 36 | return ( 37 | 38 |
    39 |
    40 |

    41 | 基本面分析 42 |

    43 |

    44 | 基于基本面的豆粕策略分析 45 |

    46 |
    47 | 48 | {/* 日期选择和查看分析按钮 */} 49 |
    50 |
    51 |
    52 | 55 | setSelectedDate(e.target.value)} 61 | min={minDate} 62 | max={today} 63 | /> 64 |
    65 |
    66 | 73 |
    74 |
    75 |
    76 | 77 | 78 |
    79 |     80 | ); 81 | }; 82 | 83 | export default OptionsStrategyPage; -------------------------------------------------------------------------------- /frontend/src/pages/PrivacyPolicy.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import Layout from '../components/layout/Layout'; 3 | 4 | const PrivacyPolicy: React.FC = () => { 5 | return ( 6 | 7 |
    8 |

    隐私政策

    9 | 10 |
    11 |
    12 |

    1. 信息收集

    13 |

    14 | 新致量化策略分析平台（以下简称"本平台"）尊重并保护用户隐私。我们可能收集以下信息： 15 |

    16 |
      17 |
    • 您提供的账户信息（如用户名、邮箱等）
      • 18 |
    • 交易策略和分析偏好设置
      • 19 |
    • 使用本平台的日志信息
      • 20 |
    • 设备信息和浏览数据
      • 21 |
    22 |
    23 | 24 |
    25 |

    2. 信息使用

    26 |

    我们使用收集的信息用于：

    27 |
      28 |
    • 提供、维护和改进我们的服务
      • 29 |
    • 开发新的服务和功能
      • 30 |
    • 个性化您的体验
      • 31 |
    • 分析平台使用情况
      • 32 |
    • 发送服务通知和更新
      • 33 |
    34 |
    35 | 36 |
    37 |

    3. 信息共享

    38 |

    我们不会出售或出租您的个人信息给第三方。在以下情况下，我们可能会分享您的信息：

    39 |
      40 |
    • 经您同意
      • 41 |
    • 与提供服务相关的合作伙伴共享（如数据处理服务商）
      • 42 |
    • 遵守法律要求、保护我们的权利或防止滥用
      • 43 |
    44 |
    45 | 46 |
    47 |

    4. 数据安全

    48 |

    49 | 我们采取合理的技术和组织措施保护您的个人信息，防止未经授权的访问、披露或滥用。然而，没有任何互联网传输或电子存储方法是100%安全的。 50 |

    51 |
    52 | 53 |
    54 |

    5. Cookie和类似技术

    55 |

    56 | 我们使用Cookie和类似技术来记住您的偏好设置、分析使用模式、优化服务体验。您可以通过浏览器设置管理Cookie偏好。 57 |

    58 |
    59 | 60 |
    61 |

    6. 您的权利

    62 |

    63 | 根据适用的数据保护法，您有权访问、更正、删除您的个人信息，并限制或反对其处理。如需行使这些权利，请联系我们。 64 |

    65 |
    66 | 67 |
    68 |

    7. 隐私政策变更

    69 |

    70 | 我们可能会更新本隐私政策以反映服务变化或法律要求。更新后的政策将在本页面公布，重大变更时我们会通知您。 71 |

    72 |
    73 | 74 |
    75 |

    8. 联系我们

    76 |

    77 | 如果您对本隐私政策有任何问题或疑虑，请通过以下方式联系我们：
    78 | 邮箱：support@soymeal-strategy.com
    79 | 电话：400-123-4567 80 |

    81 |
    82 | 83 |
    84 | 最后更新日期：2025年3月30日 85 |
    86 |
    87 |
    88 |     89 | ); 90 | }; 91 | 92 | export default PrivacyPolicy; -------------------------------------------------------------------------------- /frontend/src/pages/Research.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import Layout from '../components/layout/Layout'; 3 | import { Link } from 'react-router-dom'; 4 | import { BarChartOutlined, LineChartOutlined, FundOutlined } from '@ant-design/icons'; 5 | 6 | const Research: React.FC = () => { 7 | return ( 8 | 9 |
    10 |
    11 |

    12 | 研究报告 13 |

    14 |

    15 | 基于 DeepSeek 的豆粕市场分析和交易策略研究 16 |

    17 |
    18 | 19 |
    20 | {/* 交易策略建议卡片 */} 21 | 22 |
    23 |
    24 |
    25 |
    26 | 27 |
    28 |
    29 |

    交易策略建议

    30 |

    31 | 基于市场数据的交易策略分析和建议 32 |

    33 |
    34 |
    35 |
    36 |
    37 | 38 | 39 | {/* 核心驱动因子分析卡片 */} 40 | 41 |
    42 |
    43 |
    44 |
    45 | 46 |
    47 |
    48 |

    核心驱动因子分析

    49 |

    50 | 豆粕市场核心驱动因子的深度分析 51 |

    52 |
    53 |
    54 |
    55 |
    56 | 57 | 58 | {/* 基本面分析卡片 */} 59 | 60 |
    61 |
    62 |
    63 |
    64 | 65 |
    66 |
    67 |

    基本面分析

    68 |

    69 | 基于基本面的豆粕策略分析 70 |

    71 |
    72 |
    73 |
    74 |
    75 | 76 |
    77 |
    78 |     79 | ); 80 | }; 81 | 82 | export default Research; -------------------------------------------------------------------------------- /frontend/src/pages/StrategyAdvicePage.tsx: -------------------------------------------------------------------------------- 1 | import React, { useState, useEffect } from 'react'; 2 | import Layout from '../components/layout/Layout'; 3 | import StrategyAdvice from '../components/trading/StrategyAdvice'; 4 | import { API_ENDPOINTS } from '../config/api'; 5 | 6 | interface StrategyResponse { 7 | content: string; 8 | reasoning_content: string; 9 | } 10 | 11 | const StrategyAdvicePage: React.FC = () => { 12 | const minDate = "2025-04-10"; 13 | const today = new Date().toISOString().split('T')[0]; 14 | const initialDate = today < minDate ? minDate : today; 15 | 16 | const [selectedDate, setSelectedDate] = useState(initialDate); 17 | const [isLoading, setIsLoading] = useState(false); 18 | const [strategyData, setStrategyData] = useState(null); 19 | const [isStreaming, setIsStreaming] = useState(false); 20 | 21 | const handleViewAnalysis = async () => { 22 | setIsLoading(true); 23 | setIsStreaming(false); 24 | setStrategyData(null); 25 | 26 | try { 27 | const response = await fetch(`${API_ENDPOINTS.trading.options}?date=${selectedDate}`); 28 | 29 | // 检查响应类型 30 | const contentType = response.headers.get('content-type'); 31 | if (contentType && contentType.includes('text/event-stream')) { 32 | // 处理流式响应 33 | setIsStreaming(true); 34 | const reader = response.body?.getReader(); 35 | if (!reader) throw new Error('无法读取响应流'); 36 | 37 | const decoder = new TextDecoder(); 38 | while (true) { 39 | const { done, value } = await reader.read(); 40 | if (done) break; 41 | 42 | const chunk = decoder.decode(value); 43 | const lines = chunk.split('\n'); 44 | 45 | for (const line of lines) { 46 | if (line.startsWith('data: ')) { 47 | const data = JSON.parse(line.slice(6)); 48 | if (data.type === 'reasoning') { 49 | setStrategyData(prev => ({ 50 | reasoning_content: data.content, 51 | content: prev?.content || '' 52 | })); 53 | } else if (data.type === 'content') { 54 | setStrategyData(prev => ({ 55 | content: data.content, 56 | reasoning_content: prev?.reasoning_content || '' 57 | })); 58 | } else if (data.type === 'done') { 59 | setIsLoading(false); 60 | setIsStreaming(false); 61 | } else if (data.type === 'error') { 62 | console.error('获取策略分析失败:', data.message); 63 | setIsLoading(false); 64 | setIsStreaming(false); 65 | } 66 | } 67 | } 68 | } 69 | } else { 70 | // 处理普通响应 71 | const data = await response.json(); 72 | setStrategyData({ 73 | content: data.content, 74 | reasoning_content: data.reasoning_content 75 | }); 76 | setIsLoading(false); 77 | } 78 | } catch (error) { 79 | console.error('获取策略分析失败:', error); 80 | setIsLoading(false); 81 | setIsStreaming(false); 82 | } 83 | }; 84 | 85 | // 页面加载时调用一次查看报告接口 86 | useEffect(() => { 87 | handleViewAnalysis(); 88 | }, []); 89 | 90 | return ( 91 | 92 |
    93 |
    94 |

    95 | 交易策略建议 96 |

    97 |

    98 | 基于 DeepSeek 的豆粕市场交易策略分析 99 |

    100 |
    101 | 102 | {/* 日期选择和查看分析按钮 */} 103 |
    104 |
    105 |
    106 | 109 | setSelectedDate(e.target.value)} 115 | min={minDate} 116 | max={today} 117 | /> 118 |
    119 |
    120 | 127 |
    128 |
    129 |
    130 | 131 | 132 |
    133 |     134 | ); 135 | }; 136 | 137 | export default StrategyAdvicePage; -------------------------------------------------------------------------------- /frontend/src/pages/TermsOfService.tsx: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import Layout from '../components/layout/Layout'; 3 | 4 | const TermsOfService: React.FC = () => { 5 | return ( 6 | 7 |
    8 |

    服务条款

    9 | 10 |
    11 |
    12 |

    1. 接受条款

    13 |

    14 | 欢迎使用新致量化策略分析平台（以下简称"本平台"或"我们"）。通过访问或使用我们的服务，您同意受本服务条款的约束。如果您不同意这些条款，请勿使用本平台。 15 |

    16 |
    17 | 18 |
    19 |

    2. 服务说明

    20 |

    21 | 本平台提供豆粕市场分析、交易策略推荐、风险管理工具等服务。我们保留随时修改、暂停或终止部分或全部服务的权利，恕不另行通知。 22 |

    23 |
    24 | 25 |
    26 |

    3. 用户账户

    27 |

    28 | 您可能需要创建账户才能使用某些服务功能。您应当： 29 |

    30 |
      31 |
    • 提供准确、完整的注册信息
      • 32 |
    • 保护账户安全，对账户活动负责
      • 33 |
    • 及时更新账户信息
      • 34 |
    • 未经我们事先书面同意，不得转让账户
      • 35 |
    36 |
    37 | 38 |
    39 |

    4. 用户行为规范

    40 |

    41 | 使用本平台时，您同意不会： 42 |

    43 |
      44 |
    • 违反任何适用法律法规
      • 45 |
    • 侵犯他人知识产权或隐私权
      • 46 |
    • 上传含有病毒、木马等恶意代码的内容
      • 47 |
    • 干扰或破坏平台服务或服务器
      • 48 |
    • 未经授权收集用户信息
      • 49 |
    • 利用本平台进行欺诈活动
      • 50 |
    51 |
    52 | 53 |
    54 |

    5. 知识产权

    55 |

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    63 | 本平台提供的所有信息和分析仅供参考，不构成投资建议，不保证准确性、完整性或时效性。用户应当自行承担使用本平台进行投资决策的风险。我们对因使用本平台导致的任何损失不承担责任。 64 |

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    91 | 本服务条款受中华人民共和国法律管辖，并按其解释。与本条款相关的任何争议应提交至本平台所在地有管辖权的法院解决。 92 |

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    96 | 最后更新日期：2025年3月30日 97 |
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    99 |
    100 |     101 | ); 102 | }; 103 | 104 | export default TermsOfService; -------------------------------------------------------------------------------- /frontend/src/types/market.ts: -------------------------------------------------------------------------------- 1 | export interface ContractPrice { 2 | contract: string; 3 | price: number; 4 | historicalPrices: { 5 | date: string; 6 | open: number; 7 | high: number; 8 | low: number; 9 | close: number; 10 | volume: number; 11 | contract: string; 12 | }[]; 13 | } 14 | 15 | export interface TechnicalIndicators { 16 | contract: string; 17 | last_updated: string; 18 | current_price: number; 19 | price_targets: { 20 | support_levels: { 21 | s1: number; 22 | s2: number; 23 | }; 24 | resistance_levels: { 25 | r1: number; 26 | r2: number; 27 | }; 28 | trend: string; 29 | }; 30 | ema: { 31 | ema12: number; 32 | ema26: number; 33 | trend: string; 34 | }; 35 | macd: { 36 | diff: number; 37 | dea: number; 38 | bar: number; 39 | trend: string; 40 | }; 41 | rsi: { 42 | value: number; 43 | trend: string; 44 | }; 45 | kdj: { 46 | k: number; 47 | d: number; 48 | j: number; 49 | trend: string; 50 | }; 51 | bollinger_bands: { 52 | upper: number; 53 | middle: number; 54 | lower: number; 55 | trend: string; 56 | }; 57 | volume: { 58 | current: number; 59 | change_percent: number; 60 | trend: string; 61 | }; 62 | } 63 | 64 | export interface KlineData { 65 | date: string; 66 | open: number; 67 | high: number; 68 | low: number; 69 | close: number; 70 | volume: number; 71 | } 72 | 73 | export interface InventoryData { 74 | date: string; 75 | value: number; 76 | mom_change: number; 77 | yoy_change: number; 78 | data_type: 'inventory' | 'warehouse_receipts'; 79 | } 80 | 81 | export interface InventoryResponse { 82 | status: string; 83 | data: { 84 | inventory_list: InventoryData[]; 85 | current_status: { 86 | value: number; 87 | status: string; 88 | mom_change: number; 89 | yoy_change: number; 90 | }; 91 | }; 92 | } 93 | 94 | export interface OptionBasic { 95 | ts_code: string; 96 | exchange: string; 97 | name: string; 98 | per_unit: string; 99 | opt_code: string; 100 | opt_type: string; 101 | call_put: string; 102 | exercise_type: string; 103 | exercise_price: number; 104 | s_month: string; 105 | maturity_date: string; 106 | list_price: number; 107 | list_date: string; 108 | delist_date: string; 109 | last_edate: string; 110 | last_ddate: string; 111 | quote_unit: string; 112 | min_price_chg: string; 113 | underlying_symbol: string; 114 | } 115 | 116 | export interface OptionDaily { 117 | ts_code: string; 118 | trade_date: string; 119 | exchange: string; 120 | amount: number; 121 | close: number; 122 | high: number; 123 | low: number; 124 | oi: number; 125 | open: number; 126 | pre_close: number | null; 127 | pre_settle: number; 128 | settle: number; 129 | vol: number; 130 | } -------------------------------------------------------------------------------- /frontend/src/types/stockFutures.ts: -------------------------------------------------------------------------------- 1 | export type CompanyInfo = { 2 | code: string; 3 | name: string; 4 | type: 'upstream' | 'midstream' | 'downstream' | 'other'; 5 | description: string; 6 | }; 7 | 8 | export type StockFuturesData = { 9 | date: string; 10 | basis: number; 11 | crushingProfit: number; 12 | }; 13 | 14 | export interface SignalCondition { 15 | type: 'positive' | 'negative' | 'arbitrage'; 16 | description: string; 17 | triggered: boolean; 18 | details: { 19 | condition: string; 20 | value: number; 21 | threshold: number; 22 | met: boolean; 23 | }[]; 24 | } 25 | 26 | export interface TradingSignal { 27 | timestamp: string; 28 | type: 'positive' | 'negative' | 'arbitrage'; 29 | description: string; 30 | details?: { 31 | futuresPrice?: number; 32 | stockPrice?: number; 33 | correlation?: number; 34 | profitMargin?: number; 35 | }; 36 | conditions?: SignalCondition[]; 37 | recommendation?: { 38 | futures: { 39 | direction: 'long' | 'short'; 40 | size: number; 41 | }; 42 | stocks: { 43 | code: string; 44 | direction: 'long' | 'short'; 45 | weight: number; 46 | }[]; 47 | }; 48 | } -------------------------------------------------------------------------------- /frontend/tailwind.config.js: -------------------------------------------------------------------------------- 1 | /** @type {import('tailwindcss').Config} */ 2 | module.exports = { 3 | content: [ 4 | "./src/**/*.{js,jsx,ts,tsx}", 5 | ], 6 | theme: { 7 | extend: { 8 | colors: { 9 | primary: { 10 | 50: '#f0f9ff', 11 | 100: '#e0f2fe', 12 | 200: '#bae6fd', 13 | 300: '#7dd3fc', 14 | 400: '#38bdf8', 15 | 500: '#0ea5e9', 16 | 600: '#0284c7', 17 | 700: '#0369a1', 18 | 800: '#075985', 19 | 900: '#0c4a6e', 20 | }, 21 | }, 22 | }, 23 | }, 24 | plugins: [], 25 | } -------------------------------------------------------------------------------- /frontend/tsconfig.json: -------------------------------------------------------------------------------- 1 | { 2 | "compilerOptions": { 3 | "target": "es5", 4 | "lib": ["dom", "dom.iterable", "esnext"], 5 | "allowJs": true, 6 | "skipLibCheck": true, 7 | "esModuleInterop": true, 8 | "allowSyntheticDefaultImports": true, 9 | "strict": true, 10 | "forceConsistentCasingInFileNames": true, 11 | "noFallthroughCasesInSwitch": true, 12 | "module": "esnext", 13 | "moduleResolution": "node", 14 | "resolveJsonModule": true, 15 | "isolatedModules": true, 16 | "noEmit": true, 17 | "jsx": "react-jsx" 18 | }, 19 | "include": ["src"] 20 | } --------------------------------------------------------------------------------